2026-04-09T10:33:14Z https://tecnoscientifica.com/journal/gisa/oai

oai:oai.tecnoscientifica.com:article/29 2022-02-27T02:50:51Z gisa:ART

Analysis of Effectiveness in the Utilization and Control of Electronic Waste (E-Waste) in Indonesia Amalia, Savitri Amyas Aksar Tarigan, Ibrahim Rizkiyani, Anita Apriono, Catur Electronic waste; recycle; hazardous and toxic substances; e-waste management system In Indonesia, E-waste continues to grow rapidly, along with the increasing use of electronic goods such as telecommunications devices, households, offices, etc. Although it can be recycled, only a small portion can be done, and the recycling process is still under minimal control. Most E-waste is categorized as hazardous and toxic material waste. E-waste has a very high hazard impact if it is not recycled properly and correctly, such as polluting, damaging, and endangering the environment. This article uses forecasting of e-waste growth and canalization e-waste in Indonesia. The first data was obtained from EWasteRJ, a social community engaged in e-waste collection. The second data is obtained from questionnaires distributed to 110 respondents, focusing on knowledge and ways of handling E-waste. Using statistical analysis on both data shows that the amount of E-waste in Indonesia continues to increase every year, and public awareness of the dangers of E-waste is increasing. Tecno Scientifica Publishing 2021-11-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/29 10.53623/gisa.v1i1.29 Green Intelligent Systems and Applications; Volume 1 - Issue 1 - 2021; 1-11 2809-1116 10.53623/gisa.v1i1 eng https://tecnoscientifica.com/journal/gisa/article/view/29/22 Copyright (c) 2021 Savitri Amalia, Ibrahim Amyas Aksar Tarigan, Anita Rizkiyani, Catur Apriono https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/32 2022-02-27T02:50:51Z gisa:ART

Chest X-Ray Classification of Lung Diseases Using Deep Learning Cheah, Yew Fai CNN; COVID-19; tuberculosis; viral pneumonia Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. The classification task is separated into three and four classes, with COVID-19, normal, TB, and viral pneumonia, while the three-class problem excludes the normal lung. During testing, AlexNet and ResNet-18 gave promising results, scoring more than 95% accuracy. Tecno Scientifica Publishing 2021-11-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/32 10.53623/gisa.v1i1.32 Green Intelligent Systems and Applications; Volume 1 - Issue 1 - 2021; 12-18 2809-1116 10.53623/gisa.v1i1 eng https://tecnoscientifica.com/journal/gisa/article/view/32/28 Copyright (c) 2021 Yew Fai Cheah https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/34 2022-02-27T02:50:51Z gisa:Review

Future OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches Juwono, Filbert H. Reine, Regina OFDM; 6G; machine learning; cyber-physical social system The vision towards 6G communication networks demands higher transmission rates, massive amounts of data processing, and low-latency communication. Orthogonal Frequency Division Modulation (OFDM) has been adopted in the current 5G networks and has become one of the potential candidates for the future 6G and beyond communication systems. Although OFDM offers many benefits including high spectrum efficiency and high robustness against the multipath fading channels, it has major challenges such as frequency offset and high Peak-to-Average Power Ratio (PAPR). In order to deal with the increasingly complex communication network, Machine Learning (ML) has been increasingly utilised to create intelligent and more efficient communication network. The role of ML in dealing with frequency offset and high PAPR is discussed in this paper. In addition, ML techniques may be utilized for channel estimation, M2M networks, and biomedical applications. Finally, this paper discusses the challenges and benefits of incorporating ML into OFDM-based communication systems. Tecno Scientifica Publishing 2021-11-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/34 10.53623/gisa.v1i1.34 Green Intelligent Systems and Applications; Volume 1 - Issue 1 - 2021; 19-25 2809-1116 10.53623/gisa.v1i1 eng https://tecnoscientifica.com/journal/gisa/article/view/34/29 Copyright (c) 2021 Filbert H. Juwono, Regina Reine https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/40 2022-02-27T02:50:51Z gisa:ART

Automatic Temperature Control System on Smart Poultry Farm Using PID Method Enriko, I Ketut Agung Putra, Ryan Anugrah Estananto Smart poultry farm; temperature sensor; PID control Chicken farmers in Indonesia are facing a problem as a result of the country's harsh weather conditions. Poultry species are very susceptible to temperature and humidity fluctuations. As a result, an intelligent poultry farm is necessary to intelligently adjust the temperature in the chicken coop. A smart poultry farm is a concept in which farmers may automatically manage the temperature in the chicken coop, thereby improving the livestock's quality of life. The purpose of this research is to develop a chicken coop prototype that focuses on temperature control systems on smart poultry farms via the PID control approach. The PID control method is expected to allow the temperature control system to adapt to the temperature within the cage, thereby assisting chicken farmers in their task. The sensor utilized is a DHT22 sensor with a calibration accuracy of 96.88 percent. The PID response was found to be satisfactory for the system with Kp = 10, Ki = 0, and KD = 0.1, and the time necessary for the system to reach the specified temperature was 121 seconds with a 1.03 % inaccuracy. Tecno Scientifica Publishing 2021-11-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/40 10.53623/gisa.v1i1.40 Green Intelligent Systems and Applications; Volume 1 - Issue 1 - 2021; 37-43 2809-1116 10.53623/gisa.v1i1 eng https://tecnoscientifica.com/journal/gisa/article/view/40/31 Copyright (c) 2021 Agung Enriko, Ryan Anugrah Putra, Estananto https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/42 2022-02-27T02:50:51Z gisa:Review

Reinventing The Future Online Education Using Emerging Technologies Reine, Regina Juwono, Filbert H. Wong, W. K. Distance learning; digital twins; LMS; remote education; virtual laboratory; technology; COVID-19 The pandemic of Coronavirus Disease 2019 (COVID-19) has forced the teaching and learning activities to be conducted remotely. Before the pandemic, many academic institutions had offered online distance learning for selected courses. However, in practice, most of these programs were delivered as blended learning programs instead of full-fledged distance learning programs. Distance learning programs faced challenges and limitations in terms of communication, integrity, and interactions compared to the traditional face-to-face teaching and learning method. Despite the challenges and limitations in distance teaching and learning, academic staff is expected to accomplish the same (or better) outcomes than traditional face-to-face teaching and learning. Hence, the distance learning method was not popular with many academic staff and students before the pandemic time. In order to improve the quality of the full distance learning delivery, emerging technologies and more interactive platforms are being developed rapidly. This article discusses the emerging technologies and strategies to make full distance learning or remote education competitive compared to the traditional teaching and learning method. The future potential teaching and learning technology, i.e., digital twins, is also briefly presented. Tecno Scientifica Publishing 2021-11-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/42 10.53623/gisa.v1i1.42 Green Intelligent Systems and Applications; Volume 1 - Issue 1 - 2021; 26-36 2809-1116 10.53623/gisa.v1i1 eng https://tecnoscientifica.com/journal/gisa/article/view/42/30 Copyright (c) 2021 Regina Reine, Filbert H. Juwono, W. K. Wong https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/59 2022-04-17T05:44:11Z gisa:ART

Design of Automatic Candy Mixer using Blynk and NodeMCU ESP8266 Hugeng, Hugeng Khefin, Khefin Wulandari, Meirista Candy has many variations based on shape, texture, and taste. The more variations of the product have an effect on more consumers, Candy products also have a lot of variety, which makes mixing candy an interesting task. The mixing process of candies is usually done by weighting them manually with conventional scales, so there are some deficiencies to be improved. The automatic candy mixer using Blynk and NodeMCU ESP8266 has been designed to be able to help with the process of mixing and weighting candy automatically. This device allows users to choose weight and candy types to be mixed, whether it is one type of candy or more, from the Blynk application and is operated using a microcontroller and sensor. The utilized sensor is a load cell sensor with 1% of calibration inaccuracy. Tecno Scientifica Publishing 2022-02-27 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/59 10.53623/gisa.v2i1.59 Green Intelligent Systems and Applications; Volume 2 - Issue 1 - 2022; 1-6 2809-1116 10.53623/gisa.v2i1 eng https://tecnoscientifica.com/journal/gisa/article/view/59/54 Copyright (c) 2022 Hugeng Hugeng, Khefin Khefin, Meirista Wulandari https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/65 2022-04-17T05:44:11Z gisa:ART

Finite Impulse Response Filter for Electroencephalogram Waves Detection Melinda, Melinda Syahrial Yunidar Al Bahri Irhamsyah, Muhammad amplitude, EEG signal, filter Finite Impulse Respon Electroencephalographic data signals consist of electrical signal activity with several characteristics, such as non-periodic patterns and small voltage amplitudes that can mix with noise making it difficult to recognize. This study uses several types of EEG wave signals, namely Delta, Alpha, Beta, and Gamma. The method we use in this study is the application of an impulse response filter to replace the noise obtained before and after the FIR filter is applied. In addition, we also analyzed the quality of several types of electroencephalographic signal waves by looking at the addition of the signal to noise ratio value. In the end, the results we get after applying the filter, the noise that occurs in some types of waves shows better results. Tecno Scientifica Publishing 2022-04-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/65 10.53623/gisa.v2i1.65 Green Intelligent Systems and Applications; Volume 2 - Issue 1 - 2022; 7-19 2809-1116 10.53623/gisa.v2i1 eng https://tecnoscientifica.com/journal/gisa/article/view/65/57 Copyright (c) 2022 Melinda Melinda, Syahrial, Yunidar, Al Bahri, Muhammad Irhamsyah https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/67 2022-04-17T05:44:11Z gisa:ART

Node Localization in a Network of Doppler Shift Sensor Using Multilateral Technique Thursday Ehis, Akhigbe-mudu Localization is the process of determining the location of a target(s) in a given set of coordinates using a location system.However, due to environmental uncertainty and Doppler effects, mistakes in distance estimations are created in physical situations, resulting in erroneous target location. A range-based multilateration technique is presented to improve localization accuracy. Multilateration is the method of calculating a position based on the range measurements of three or more anchors, with each satellite acting as the sphere's center. The distance between the satellite and the receiver is represented by the sphere's radius. The intersection of four spherical surfaces determines the receiver's position. This study's approach proposes a simple measure for evaluating GRT based on reference node selection. The algorithm utilizes these reference nodes, seeking to determine the optimal location based on ranging error. It calculates GRT values for each of the three node combinations. This study evaluates the performance of range-based localization using the Multilateration Algorithm with a Correcting Factor. The correction factor is applied to both the anchor node and the node to be measured; hence, the localization error is significantly reduced. In terms of how much time and money it takes to run and how much hardware it costs, the new method is better than some of the current methods. Tecno Scientifica Publishing 2022-04-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/67 10.53623/gisa.v2i1.67 Green Intelligent Systems and Applications; Volume 2 - Issue 1 - 2022; 20-33 2809-1116 10.53623/gisa.v2i1 eng https://tecnoscientifica.com/journal/gisa/article/view/67/58 Copyright (c) 2022 Akhigbe-mudu Thursday Ehis https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/69 2022-04-17T05:44:11Z gisa:ART

Development of Hot Air Dryer Conveyor for Automotive Tampo Printing Parts Rospawan, Ali Simatupang, Joni Welman Purnama, Irwan This paper presents the development of a hot air dryer conveyor for the automotive industry in the tampo printing part of the process. The research started by designing and creating the actual device that is ready to use and be implemented in the industry. The method provided details on the drying chamber, hot air dryer, and their mathematical model. The chosen hot air dryer operated in the factory default of auto-tuning mode. The performance evaluation studies indicated the performance of the hot air dryer for the chosen size of the drying chamber, the robustness of the system against fluctuating environmental air change rate, the ducting capacity, and the damper opening value estimation performance. The result of this system was working well at the specification requirement of operating at an air change rate of 15 to 21 while working at 80% of its maximum capacity, and the equipment has been successfully implemented. The detailed results are that the conveyor is only working while the settled temperature was achieved and the full work sensor is off, the hot air dryer perfectly matches the chamber size, and the chamber size selection was also well calculated and implemented. Tecno Scientifica Publishing 2022-04-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/69 10.53623/gisa.v2i1.69 Green Intelligent Systems and Applications; Volume 2 - Issue 1 - 2022; 34-41 2809-1116 10.53623/gisa.v2i1 eng https://tecnoscientifica.com/journal/gisa/article/view/69/59 Copyright (c) 2022 Ali Rospawan, Joni Welman Simatupang, Irwan Purnama https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/78 2022-04-17T05:45:10Z gisa:ART

Sarawak Traditional Dance Motion Analysis and Comparison using Microsoft Kinect V2 Gau, Michael-Lian Ting, Huong Yong Ting, Jackie Tiew-Wei Peter, Marcella Ibrahim, Khairunnisa Microsoft Kinect Sarawak Dance Motion Analysis Motion Comparison This research project aimed to develop a software program or an interactive dance motion analysis application that utilizes modern technology to preserve and maintain the Sarawak traditional dance culture. The software program employs the Microsoft Kinect V2 to collect the digital dance data. The proposed method analyses the collected dance data for comparison purposes only. The comparison process was executed by displaying a traditional dance on the screen where the user who wants to learn the traditional dance can follow it and obtain results on how similar the dance is compared to the recorded dance data. The comparison of the performed and recorded dance data was visualized in graph form. The comparison graph showed that the Microsoft Kinect V2 sensors were capable of comparing the dance motion but with minor glitches in detecting the joint orientation. Using better depth sensors would make the comparison more accurate and less likely to have problems with figuring out how the joints move. Tecno Scientifica Publishing 2022-04-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/78 10.53623/gisa.v2i1.78 Green Intelligent Systems and Applications; Volume 2 - Issue 1 - 2022; 42-52 2809-1116 10.53623/gisa.v2i1 eng https://tecnoscientifica.com/journal/gisa/article/view/78/64 Copyright (c) 2022 Michael-Lian Gau, Huong Yong Ting, Jackie Tiew-Wei Ting, Marcella Peter, Khairunnisa Ibrahim https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/95 2022-12-06T02:55:25Z gisa:Review

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review Mulyono, Aditya Eka Apnitami, Priska Wangi, Insani Sekar Wicaksono, Khalfan Nadhief Prayoga Apriono, Catur systematic review coffee agriculture smart farming iot internet of things PSALSAR As one of Indonesia’s main export agricultural commodities, coffee farming faces many obstacles, ranging from plant pest organisms to climate and environmental problems. These problems can be solved using smart farming technology. However, smart farming technology has not been applied intensively in Indonesia. This paper aims to analyze the potential for implementing smart farming for coffee in Indonesia. This article presents a systematic review of the information about the potential application of IoT smart farming for coffee farming in Indonesia by applying the PSALSAR (Protocol, Search, Appraisal, Synthesis, Analysis, Report) review method. This study concludes the list of smart farming technologies for coffee that have the potential to be applied in Indonesia. Those technologies are classified based on their application scope: quality control (including subtopics like coffee quality control), climate monitoring, the anticipation of pest organisms, and coffee processing), coffee production planning, and coffee waste utilization. Regarding infrastructure readiness and the need for smart farming technology for coffee, the island of Java has the most potential for implementing smart farming for coffee as soon as possible. The high potential for application in Java is because the telecommunications technology infrastructure is ready, and the land area and coffee production are large. Tecno Scientifica Publishing 2022-08-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/95 10.53623/gisa.v2i2.95 Green Intelligent Systems and Applications; Volume 2 - Issue 2 - 2022; 53-70 2809-1116 10.53623/gisa.v2i2 eng https://tecnoscientifica.com/journal/gisa/article/view/95/83 Copyright (c) 2022 Aditya Eka Mulyono, Priska Apnitami, Insani Sekar Wangi, Khalfan Nadhief Prayoga Wicaksono, Catur Apriono https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/106 2022-12-06T02:55:25Z gisa:ART

Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems Rospawan, Ali Yang, Yukai Chen, Po-Hsu Tsai, Ching-Chih This paper presents a case study of the setpoint tracking performance of the proportional integral derivative (PID) controller on the Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) nonlinear digital plants under Gaussian white noise and constant load disturbance for the nonlinear time-delay dynamic system. With the objective of getting a better understanding of the nonlinear discrete-time PID controller, we proposed a case study using two SISO and two MIMO digital plants, and then do the numerical simulations along with the addition of Gaussian white noise and load disturbance to simulate the real environment. In this paper, we compare the results of the system working with and without noise and load disturbance. The study result of this paper shows that on the discrete-time digital nonlinear plant, the PID controller is working well to follow the nonlinear setpoint even under heavy noise and load disturbance. The study compared the performance indexes of the controllers in terms of the maximum error, the Root mean square error (RMSE), the Integral square error (ISE), the Integral absolute error (IAE), and the Integral of time-weighted absolute error (ITAE). Tecno Scientifica Publishing 2022-10-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/106 10.53623/gisa.v2i2.106 Green Intelligent Systems and Applications; Volume 2 - Issue 2 - 2022; 84-95 2809-1116 10.53623/gisa.v2i2 eng https://tecnoscientifica.com/journal/gisa/article/view/106/89 Copyright (c) 2022 Ali Rospawan, Yukai Yang, Po-Hsu Chen, Prof. Tsai https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/109 2022-12-06T02:55:25Z gisa:ART

Attendance System with Face Recognition, Body Temperature, and Use of Mask using Multi-Task Cascaded Convolutional Neural Network (MTCNN) Method Basjaruddin, Noor Cholis Rakhman, Edi Sudarsa, Yana Asyikin, Moch Bilal Zaenal Permana, Septia covid-19, attendance system, facial recognition, mask detection, body temperature, MTCNN The application of health protocols in educational, office, or industrial environments can be made by changing old habits that can spread COVID-19. One of them is the habit of recording attendance, which still requires direct physical contact. In this research, an attendance system based on facial recognition, body temperature checks, and mask use using the multi-task cascaded convolutional neural network (MTCNN) has been developed. This research aims to integrate a facial recognition system, a mask detection system, and body temperature reading into an attendance recording system without the need for direct physical contact. The attendance system offered in this study can minimize the spread of COVID-19. So, it has enormous potential for use in educational, office, and industrial environments. The focus of this research is to create an attendance system by integrating the application of face recognition, body temperature, and the use of masks using a pre-trained model. Based on the research results, an attendance system was successfully developed where the results of face recognition, mask detection, and body temperature were displayed on the machine screen and attendance platform. Facial recognition testing on the original LFW dataset has an accuracy of 66.45%. The accuracy of the dataset reaches 92-100%. In addition, the intelligent attendance platform has been successfully developed with user management, machine service, and attendance service features. The results of the attendance record are successfully displayed on the platform or through the download feature. Tecno Scientifica Publishing 2022-10-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/109 10.53623/gisa.v2i2.109 Green Intelligent Systems and Applications; Volume 2 - Issue 2 - 2022; 71-83 2809-1116 10.53623/gisa.v2i2 eng https://tecnoscientifica.com/journal/gisa/article/view/109/90 Copyright (c) 2022 Noor Cholis Basjaruddin, Edi Rakhman, Yana Sudarsa, Moch Bilal Zaenal Asyikin, Septia Permana https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/111 2022-12-06T02:55:25Z gisa:ART

Development of COVID-19 Isolation Facility Management System with Scrum Framework Darmowinoto, Sandy Hossain, Syed Rafi Astuti, Puji COVID-19; information system; isolation facility; scrum A COVID-19 pandemic hit Indonesia in early 2020, and on the 31st of March 2020, President Joko Widodo declared a public health emergency. By June 2021, the Delta variant hit Indonesia, causing shortages of hospital beds and resources. People who were tested positive for COVID-19 were asked to self-isolate at home. However, many houses in Indonesia are not suitable for self-isolation. Meanwhile, President University’s and President Community College’s students’ dormitories were empty as students returned to their homes and resumed their studies remotely using online classes. Therefore, the President University Foundation decided to repurpose the students’ dormitories as COVID-19 isolation facilities. To support its daily operation, an isolation facility management system was developed. To ensure the timely delivery of the system, Scrum was chosen as its development framework. Ten (10) participants tested the system for its usability, and the system scored an average of 94.5. This indicates that the developed system is easy to use and highly usable. The system was completed within a month, according to the planned schedule. The use of the Scrum framework has allowed the development team to produce a useful and effective information system in the shortest amount of time possible. Therefore, the system developed by this research provides services and facilities that are not only important in helping COVID-19 patients but also a better environment and has an integrated information system with various parties involved in handling COVID-19 patients. Tecno Scientifica Publishing 2022-10-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/111 10.53623/gisa.v2i2.111 Green Intelligent Systems and Applications; Volume 2 - Issue 2 - 2022; 96-107 2809-1116 10.53623/gisa.v2i2 eng https://tecnoscientifica.com/journal/gisa/article/view/111/97 Copyright (c) 2022 Sandy Darmowinoto, Syed Rafi Hossain, Puji Astuti https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/115 2022-12-06T02:55:25Z gisa:Review

Big Data in Supply Chain Management: A Systematic Literature Review Runtuk, Johan Krisnanto Sidjabat, Filson Jsslynn Jordan, Felicia Big data analytics (BDA) have the potential to improve upon and change conventional supply chain management (SCM) techniques. Using BDA, organisations need to build the necessary skills to use big data effectively. Since BDA is relatively new and has few practical applications in SCM and logistics, a systematic review is needed to emphasise the most significant advancements in current research. The objectives are to evaluate and categorise the literature that addresses the big data potential in SCM and the current practises of big data in SCM. The Systematic Literature Review (SLR) was conducted to analyse several published papers between 2017 and 2022. It follows four steps: the literature collection, descriptive analysis, category selection, and material evaluation in a systematic review. The finding reveals that BDA has been applied in many supply chain functions. Furthermore, integrating BDA in SCM has several advantages, including improved data analytics capabilities, logistical operation efficiency, supply chain and logistics sustainability, and agility. Finally, the study emphasises the importance of using BDA to support the success of SCM in businesses. Tecno Scientifica Publishing 2022-11-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/115 10.53623/gisa.v2i2.115 Green Intelligent Systems and Applications; Volume 2 - Issue 2 - 2022; 108-117 2809-1116 10.53623/gisa.v2i2 eng https://tecnoscientifica.com/journal/gisa/article/view/115/99 Copyright (c) 2022 Johan K. Runtuk, Filson Sidjabat, Jsslynn, Felicia Jordan https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/229 2025-04-24T06:10:14Z gisa:ART

Effectiveness of Using Artificial Intelligence for Early Child Development Screening Gau, Michael-Lian Ting, Huong-Yong Toh, Teck-Hock Wong, Pui-Ying Woo, Pei-Jun Wo, Su-Woan Tan, Gek-Ling This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results. Tecno Scientifica Publishing 2023-05-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/229 10.53623/gisa.v3i1.229 Green Intelligent Systems and Applications; Volume 3 - Issue 1 - 2023; 1-13 2809-1116 10.53623/gisa.v3i1 eng https://tecnoscientifica.com/journal/gisa/article/view/229/134 Copyright (c) 2023 Michael-Lian Gau, Huong-Yong Ting, Teck-Hock Toh, Pui-Ying Wong, Pei-Jun Woo, Su-Woan Wo, Gek-Ling Tan https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/244 2025-04-24T06:10:14Z gisa:ART

Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification Samijayani, Octarina Nur Saputra, Tyan Permana Firdaus, Hamzah Mujadin, Anwar Wireless Sensor Networks Green WSN Solar Energy Harvesting Water Quality Water is essential for human being, also for animals and plants. In Indonesia, there are a lot of residential living in the riverbank which have poor water conditions. People frequenty use water from the river for daily activities. To determine the quality of water, samples are usually taken and tested in the laboratory. This method is less efficient in time and also cost. In order to determine and monitor the quality of water, this paper discuss the Wireless Sensor Network (WSN) to monitor the quality of water from a distance with the self powered sensor node. One of the issue in developing the WSN is the energy. Since this is implemented in outdoor, therefore it is possible to use solar panel to produce the energy. In this study three indicators; pH, TDS, and turbidity; were used to determine water quality based on the Indonesian Minister of Health Regulation. The results examine the WSN performance, and also the analysys of the solar energy supply for each sensor node. The WSN successfully works in detect and clasify tha water quality category and display it in the monitoring center or user. The sensors are calibrated and works with tolerable error of sensor reading of 5,1%. The WSN node is embedded with solar panel to supply the energy for node component. Therefore it able to extend the lifetime of the networks devices with renewable energy to implement the Green WSN. Tecno Scientifica Publishing 2023-05-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/244 10.53623/gisa.v3i1.244 Green Intelligent Systems and Applications; Volume 3 - Issue 1 - 2023; 14-21 2809-1116 10.53623/gisa.v3i1 eng https://tecnoscientifica.com/journal/gisa/article/view/244/135 Copyright (c) 2023 Octarina Nur Samijayani, Tyan Permana Saputra, Hamzah Firdaus, Anwar Mujadin https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/249 2025-04-24T06:10:14Z gisa:ART

Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate Akbar, Teuku Alif Rafi Apriono , Catur Customer churn frequently occurs in the telecommunications industry, which provides services and can be detrimental to companies. A predictive model can be useful in determining and analyzing the causes of churn actions taken by customers. This paper aims to analyze and implement machine learning models to predict churn actions using Kaggle data on customer churn. The models considered for this research include the XG Boost Classifier algorithm, Bernoulli Naïve Bayes, and Decision Tree algorithms. The research covers the steps of data preparation, cleaning, and transformation, exploratory data analysis (EDA), prediction model design, and analysis of accuracy, F1 Score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. The EDA results indicate that the contract type, length of tenure, monthly invoice, and total bill are the most influential features affecting churn actions. Among the models considered, the XG Boost Classifier algorithm achieved the highest accuracy and F1 score of 81.59% and 74.76%, respectively. However, in terms of efficiency, the Bernoulli Naïve Bayes and Decision Tree algorithms outperformed XG Boost, with AUC scores of 0.7469 and 0.7468, respectively. Tecno Scientifica Publishing 2023-06-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/249 10.53623/gisa.v3i1.249 Green Intelligent Systems and Applications; Volume 3 - Issue 1 - 2023; 22-34 2809-1116 10.53623/gisa.v3i1 eng https://tecnoscientifica.com/journal/gisa/article/view/249/146 Copyright (c) 2023 Teuku Alif Rafi Akbar, Catur Apriono https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/251 2025-04-24T06:10:14Z gisa:ART

Real-Time Web-based Dashboard using Firebase for Automated Object Detection Applied on Conveyor Afira, Fadhillah Simatupang, Joni Welman Conveyor Webcam Firebase Database Dashboard Conveyors are used by many factories in the industrial sector as tools to move some materials through various processes. Currently, it is necessary to have a device which is connected to a conveyor using a digital system. In this study, a conveyor is designed to use a webcam with a deep learning image classification system, Firebase real-time database, and a web-based dashboard. The webcam is used to capture and classify objects based on shape, color, and status, as well as counting objects that run on the conveyor. Firebase real-time database will receive and store data from the webcam system in real-time so that the data can be displayed on the dashboard. The dashboard used is a website-based design using two web development systems: front-end and back-end. Data displayed on the dashboard uses a real-time data table which is capable of displaying real-time data. Testing is conducted to analyze the performance of the full prototype. Testing methods used are One-by-one Object Test and Sequential Object Test, with total of 20 tests. One-by-one Object test is conducted five times, with a total of 168 data and a total time of 12 minutes and 15 seconds. Meanwhile, Sequential Object test is conducted 15 times, with a total of 546 data and a total time of 7 minutes and 19 seconds. Based on the observations of functional dashboard test, in fact all features and buttons on the dashboard are functioned well. Tecno Scientifica Publishing 2023-06-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/251 10.53623/gisa.v3i1.251 Green Intelligent Systems and Applications; Volume 3 - Issue 1 - 2023; 35-47 2809-1116 10.53623/gisa.v3i1 eng https://tecnoscientifica.com/journal/gisa/article/view/251/148 Copyright (c) 2023 Fadhillah Afira, Joni Welman Simatupang https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/256 2025-04-24T06:10:14Z gisa:ART

Light Weight Native Edge Load Balancers for Edge Load Balancing Ravi Kumar, P. Rajagopalan, S. Charles P., Joseph Edge Comuting TCP (Transmission Control Protocol) HTTS (Hypher Text Transfer Protocol Secure) NELB (Native Edge Load Balancer) SSL (Secure Socket Layer) TSL (Transport Layer Security) IoT (Ineternet of Things) and DDoS (Distributed Denial of Service) Edge computing has become an essential aspect of modern computing systems. Edge computing involves processing data at the edge of the network, closer to where the data is generated. The ability to process data in real-time at the edge provides various benefits such as lower latency, improved response times, and reduced network congestion. Load balancing is a critical component of edge computing, which distributes the workload across multiple edge devices, ensuring that the workload is evenly distributed. This paper discusses current trends in edge computing load balancing techniques, including static, dynamic, and hybrid load balancing approaches. Tecno Scientifica Publishing 2023-06-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/256 10.53623/gisa.v3i1.256 Green Intelligent Systems and Applications; Volume 3 - Issue 1 - 2023; 48-55 2809-1116 10.53623/gisa.v3i1 eng https://tecnoscientifica.com/journal/gisa/article/view/256/149 Copyright (c) 2023 P. Ravi Kumar, S. Rajagopalan, Joseph Charles P. https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/269 2023-11-28T12:03:38Z gisa:ART

Android Based College App Using Flutter Dart Marimuthu, Kavitha Panneerselvam, Arunkumar Selvaraj, Senthilkumar Venkatesan, Lakshmi Praba Sivaganesan, Vetriselvi App for college management, Android, Flutter, Dart In today's world, communication and information sharing between teachers and students have increasingly shifted to online platforms such as Google Classroom, Gmail, Google Forms, WhatsApp, and more. To address the diverse needs of educational institutions, we developed an app that supports all devices, including mobile phones, laptops, and tablets. The Android app for mobile and tablet websites supports all devices seamlessly. This app provides comprehensive information on attendance, examination schedules, lecture notes, fee details, event notifications, and online tests, catering to all the requirements of the institution. We developed this app using the latest technology, including Flutter and Dart, with Firebase integration. Additionally, we created a web application that is easily accessible via desktops. This website, along with the app, is connected to the same Firebase server, ensuring synchronized data access. The institute has taken a step further by developing its own Android application and website to enhance efficient communication with its students. These platforms are exclusively accessible and available to authorized users associated with the institute, ensuring privacy and security. Tecno Scientifica Publishing 2023-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/269 10.53623/gisa.v3i2.269 Green Intelligent Systems and Applications; Volume 3 - Issue 2 - 2023; 69-85 2809-1116 10.53623/gisa.v3i2 eng https://tecnoscientifica.com/journal/gisa/article/view/269/161 Copyright (c) 2023 Kavitha Marimuthu, Arunkumar Panneerselvam, Senthilkumar Selvaraj, Lakshmi Praba Venkatesan, Vetriselvi Sivaganesan https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/270 2023-11-28T12:03:38Z gisa:ART

Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern Melinda, Melinda Yunidar, Yunidar Andryani, Nur Afny Catur fluctuation pattern High High- fluctuation Convolutional Neural Network In the acquisition of amplitude data, the inaccuracy of a signal with the occurrence of an unstable peak value of the amplitude in the data is called a fluctuation. This study uses High-High Fluctuation (HHF) signal data from the acquisition of Multi-Spectral Capacitive Sensors (MSCS) with Hydrogen Dioxide (H2O) and Hydrogen Dioxide (H2O) objects mixed with Sodium Hydroxide (NaOH) that have been organized into a matrix form. The data acquisition results in previous studies have several parts that are difficult to distinguish with the naked eye. The method used in this study applies the CNN method for image recognition of signal fluctuations of type HHF from H2O and H2O mixed with NaOH, using the architecture known as AlexNet. Then, the H2O and H2O data groups mixed with NaOH were grouped into training data groups of 280 image data for each data type, and 70 image data for test data for both groups. During the training stage, the number of epochs used is 20. However, by the time the number of epochs reaches 15, the accuracy rate is already high, reaching 98%. Furthermore, at the testing stage, the CNN program can correctly recognize the entire 70 image data for both materials, achieving perfect recognition for the total amount of the two materials. Tecno Scientifica Publishing 2023-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/270 10.53623/gisa.v3i2.270 Green Intelligent Systems and Applications; Volume 3 - Issue 2 - 2023; 56-68 2809-1116 10.53623/gisa.v3i2 eng https://tecnoscientifica.com/journal/gisa/article/view/270/160 Copyright (c) 2023 Melinda Melinda, Yunidar Yunidar, Nur Afny Catur Andryani https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/313 2023-11-28T12:03:38Z gisa:ART

The Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection Kakade, Suhas Kulkarni, Rohan Dhawale, Somesh Fasil C, Muhammed Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures. Tecno Scientifica Publishing 2023-11-03 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/313 10.53623/gisa.v3i2.313 Green Intelligent Systems and Applications; Volume 3 - Issue 2 - 2023; 86-97 2809-1116 10.53623/gisa.v3i2 eng https://tecnoscientifica.com/journal/gisa/article/view/313/166 Copyright (c) 2023 Suhas Kakade, Rohan Kulkarni, Somesh Dhawale, Muhammed Fasil C https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/323 2023-11-28T12:03:38Z gisa:ART

IoT-based Heart Signal Processing System for Driver Drowsiness Detection Yunidar, Yunidar Melinda, Melinda Khairani, Khairani Irhamsyah, Muhammad Basir, Nurlida Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers. Tecno Scientifica Publishing 2023-11-26 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/323 10.53623/gisa.v3i2.323 Green Intelligent Systems and Applications; Volume 3 - Issue 2 - 2023; 98-110 2809-1116 10.53623/gisa.v3i2 eng https://tecnoscientifica.com/journal/gisa/article/view/323/169 Copyright (c) 2023 Yunidar Yunidar, Melinda Melinda, Khairani Khairani, Muhammad Irhamsyah, Nurlida Basir https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/325 2023-11-28T12:03:38Z gisa:ART

Enhanced IoT Solution System for Smart Agriculture in Indonesia Hugeng, Hugeng Trisnawarman, Dedi Huntarso, Axel Irving Yoshua Internet of things, Smart agriculture, Android application, Arduino mega This innovative solution encompasses an IoT-based smart agricultural system. The system includes a solar panel power supply, a weather station (monitoring temperature, humidity, air pressure, wind speed and direction, raindrop), an air quality monitoring module (measuring NH4, CO2, and PM2.5 levels), a soil quality measurement module, a microcontroller, a GSM cellular module for internet connectivity, and an automated relay actuator for a water pump. The water pump's operation is contingent upon the soil moisture levels, ensuring efficient irrigation. The utilization of an IoT-driven smart agricultural system enables real-time monitoring of weather conditions, air quality, and agricultural soil conditions. Additionally, it facilitates the remote control of automated water pumps via smartphones—an aspect that remains unattainable within the confines of traditional Indonesian agriculture. Leveraging an Android application on smartphones, this system delivers detailed insights. To present the collected sensor data in accordance with prevailing environmental and soil states, a dedicated Android application has been developed. Moreover, this application facilitates the control of the water pump to irrigate arid soil as required. The data is transmitted via the internet to a cloud server, serving as the intermediary that receives data from the IoT system's sensors positioned at the farm. Tecno Scientifica Publishing 2023-11-26 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/325 10.53623/gisa.v3i2.325 Green Intelligent Systems and Applications; Volume 3 - Issue 2 - 2023; 111‒125 2809-1116 10.53623/gisa.v3i2 eng https://tecnoscientifica.com/journal/gisa/article/view/325/170 Copyright (c) 2023 Hugeng Hugeng, Dedi Trisnawarman, Axel Irving Yoshua Huntarso https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/342 2024-06-22T09:30:18Z gisa:ART

Internet of Things and Web-App-Based Data Accessibility and Management System for Chromameter Sensor Database Samsuri, Faisal Simatupang, Joni Welman Internet of Things; Web Apps; Data Management; Database; QR Code Information technology, an integral part of most industrial activities, is essential for supporting the rapid and substantial processes of the industrial sector. A key component of this technology is the Internet of Things (IoT), which is extensively integrated into these systems. At PT Sugity Creatives, an analysis revealed impractical methods in the production process, such as manual data recording and input, as well as the use of stickers on the rear side of product bumpers. These stickers can be detached from the main body (car) for verification purposes. To improve these processes, a data accessibility and management system were incorporated into a Raspberry Pi-based chromameter sensor prototype. This integrated system is designed to collect and store data in a database and uniquely identify data using QR Codes. The system takes an average of 7.97 seconds to store data and generate a QR Code per entry. This includes a module processing time of 7.25 seconds per data point and a rapid transmission rate of 0.72 seconds, covering data recording and QR Code transmission from the chromameter prototype, with data sizes ranging between 700 to 750 bytes. Tecno Scientifica Publishing 2024-03-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/342 10.53623/gisa.v4i1.342 Green Intelligent Systems and Applications; Volume 4 - Issue 1 - 2024; 29-40 2809-1116 10.53623/gisa.v4i1 eng https://tecnoscientifica.com/journal/gisa/article/view/342/206 Copyright (c) 2024 Faisal Samsuri, Joni Welman Simatupang https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/352 2024-06-22T09:30:18Z gisa:Review

Durian Species Classification Using Deep Learning Method Teo, Boon Chen Ting, Huong Yong Atanda, Abdulwahab Funsho Durian Image Classification Artificial Intelligence Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy. Tecno Scientifica Publishing 2024-01-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/352 10.53623/gisa.v4i1.352 Green Intelligent Systems and Applications; Volume 4 - Issue 1 - 2024; 1-10 2809-1116 10.53623/gisa.v4i1 eng https://tecnoscientifica.com/journal/gisa/article/view/352/192 Copyright (c) 2024 Boon Chen Teo, Huong Yong Ting, Abdulwahab Funsho Atanda https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/355 2024-06-22T09:30:18Z gisa:Review

Enhancing Supply Chain Traceability through Blockchain and IoT Integration: A Comprehensive Review Wong, Elton Kee Sheng Ting , Huong Yong Atanda, Abdulwahab Funsho Supply chain traceability is essential for ensuring safety, preventing counterfeit goods, and improving efficiency. The integration of blockchain technology and the Internet of Things (IoT) has emerged as a transformative approach to enhance supply chain traceability by creating a secure, transparent, and efficient way to track the movement of goods and materials. This comprehensive literature review examines how the integration of blockchain and the Internet of Things can enhance supply chain traceability, utilizing a systematic literature search to identify and analyze all relevant studies. Recent and related articles selected from the Scopus database were reviewed. Our analysis underscores the potential for blockchain and IoT integration to provide end-to-end visibility, secure data sharing, and real-time monitoring across the supply chain ecosystem. It also identifies Machine Learning (ML) as another key component that enhances the security challenges of the Internet of Things while simultaneously serving as an analytical tool in Supply Chain Management (SCM). The review concludes that the integration of blockchain, the Internet of Things, and ML has the potential to transform supply chain traceability. By providing a secure, transparent, and efficient way to track the movement of goods and materials, businesses can improve their operations and offer better products and services to their customers. However, these findings do not impact the results of this research work. Additional research and a more extensive examination of the literature could offer a more comprehensive insight into the subject matter. Tecno Scientifica Publishing 2024-02-06 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/355 10.53623/gisa.v4i1.355 Green Intelligent Systems and Applications; Volume 4 - Issue 1 - 2024; 11-28 2809-1116 10.53623/gisa.v4i1 eng https://tecnoscientifica.com/journal/gisa/article/view/355/195 Copyright (c) 2024 Elton Kee Sheng Wong, Huong Yong Ting , Abdulwahab Funsho Atanda https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/417 2024-10-28T02:14:45Z gisa:ART

Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection Benitez, Ralph Andrei A. Acula, Donata D. Bondoc, Anton Oliver M. Hizon, Angelo Louis L. Santos, Aaron Joseph D. Machine Learning Handwritten Text Recognition Convolutional Neural Network Canny Edge Detection Medical Prescription AlexNet Misinterpreted medical prescriptions had led to casualties due to the illegible cursive handwriting of medical practitioners. Many studies focused on this problem. However, the accuracy was unsatisfactory and needed improvement. The study evaluated the performance of the Canny edge detection with other preprocessing methods, including RGB to Grayscale Conversion, Binarization, and Inversion, which was used to process the images of cursive handwritten medical prescriptions using Alexnet Convolutional Recurrent Neural Network (ACoRNN). The CRNN model developed by previous researchers was used as the basis for comparison, and the researchers created a faster and more accurate model. The best combination of preprocessing methods for ACoRNN was with RGB to Grayscale Conversion, Binarization, Canny edge detection, and Inversion. The researchers’ model had faster preprocessing and testing time and achieved 90.76% average accuracy through five trials. Tecno Scientifica Publishing 2024-06-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/417 10.53623/gisa.v4i1.417 Green Intelligent Systems and Applications; Volume 4 - Issue 1 - 2024; 41-53 2809-1116 10.53623/gisa.v4i1 eng https://tecnoscientifica.com/journal/gisa/article/view/417/229 Copyright (c) 2024 Ralph Andrei A. Benitez, Donata D. Acula, Anton Oliver M. Bondoc, Angelo Louis L. Hizon, Aaron Joseph D. Santos https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/457 2024-12-12T00:58:50Z gisa:ART

Comparative Study of Base Transceiver Stations Infrastructure Planning Using Machine Learning for Under Construction Area: A Case Study of Ibu Kota Nusantara Yustin, Alfiyah Shaldzabila Apriono, Catur BTS Machine Learning Projection Random Forest Regression Communication is a fundamental human need that occurs directly or through technologies like telephones and signal transmitters such as BTS and satellites. Satellites, including Starlink, serve as additional solutions for internet access needs, particularly in remote areas, albeit higher costs remain a factor necessitating conventional BTS infrastructure development. Telecommunication operators face challenges in constructing BTS in areas with limited access and complex financial considerations due to low demand in rural areas, requiring careful planning. This study utilizes several supporting variables with the aid of machine learning techniques such as Linear Regression, SVR, Random Forest, and Gradient Boosting to forecast BTS requirements. Comparative analysis shows that the random forest machine learning method provides the best modeling results compared to linear regression, Gradient Boosting, and SVR methods. Despite the superior performance of the random forest method, further fine-tuning is still needed through parameter adjustments and evaluation of variables used to achieve an even better model. The modeling results can be utilized to predict the BTS infrastructure needs in IKN, estimated at 61,135 units. In BTS development planning, mobile operators can collaborate both among themselves and with Internet Service Providers (ISPs) utilizing satellite media. Utilizing shared towers can be an option for cost-efficient BTS infrastructure development. Tecno Scientifica Publishing 2024-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/457 10.53623/gisa.v4i2.457 Green Intelligent Systems and Applications; Volume 4 - Issue 2 - 2024; 54-65 2809-1116 10.53623/gisa.v4i2 eng https://tecnoscientifica.com/journal/gisa/article/view/457/233 Copyright (c) 2024 Alfiyah Shaldzabila Yustin, Catur Apriono https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/491 2024-12-12T00:58:50Z gisa:ART

A Sentiment Analysis of Hate Speech in Philippine Election-Related Posts Using BERT Combined with Convolutional Neural Networks and Model Variations Incorporating Hashtags and ALL-CAPS Mendoza, Micah Collette O. Nadurata, Wayne Gabriel S. Oritz, Mark Gabriel E. Padlan, Joshua Mari L. Ponay, Charmaine S. As the number of people who use X continually increases, the same thing is true for hate speech. A pressing need exists for automatic detection of posts that promote hate speech. The datasets gathered and validated from the base study were used to categorize posts as either hate or non-hate and classify them as positive, negative, or neutral using Conventional Neural Networks. The partitioning of the labeled data into training and testing sets adhered to a ratio scheme: 70%-30%, 80%-20%, and 90%-10%. The model of this study, BERT-CNN, had an overall better performance than the base study, fastText CNN. Notably, among the three splits, the BERT-CNN model for binary classification without the features of Hashtags and ALL-CAPS with the 90:10 split achieved the best performance with an accuracy of 93.55%, precision of 93.59%, and F1-score of 93.55%. For multi-label classification, the BERT-CNN model demonstrated its optimal performance when incorporating hashtags, specifically with the 90:10 split, achieving an accuracy of 69.14%, precision of 68.44%, recall of 68.40%, and an F1-score of 67.41%. The innovative use of BERT word embeddings paired with CNN proved to excel in classifying Philippine election-related posts as hate or non-hate. Tecno Scientifica Publishing 2024-10-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/491 10.53623/gisa.v4i2.491 Green Intelligent Systems and Applications; Volume 4 - Issue 2 - 2024; 66-79 2809-1116 10.53623/gisa.v4i2 eng https://tecnoscientifica.com/journal/gisa/article/view/491/251 Copyright (c) 2024 Micah Collette O. Mendoza, Wayne Gabriel S. Nadurata, Mark Gabriel E. Oritz, Joshua Mari L. Padlan, Charmaine S. Ponay https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/502 2024-12-12T00:58:50Z gisa:ART

Radiation Performance Comparison and Analysis of Ku-band Microstrip Antennas with Diamond, Octagonal, and Circular Array Configurations Adriansyah, Muhammad Athallah Wahdiyat, Aditya Inzani Apriono, Catur Phased array antennas are essential in modern communication systems, particularly within the Ku-band, which is widely used for satellite communications and radar applications due to its high data rate capabilities. This paper explores the radiation characteristics of Ku-band microstrip antennas arranged in diamond, octagonal, and circular arrays, focusing on uniform excitation to ensure consistency across evaluations. Using CST Microwave Studio 2024 for simulations, the study found that the rectangular array provides the highest gain and narrowest beamwidth, making it suitable for applications where directional accuracy is critical. However, this configuration also resulted in higher sidelobe levels, which can be problematic in environments where minimal interference is required. The diamond array, while exhibiting lower gain, achieved superior sidelobe suppression, making it ideal for scenarios where reducing interference is prioritized over maximizing directivity. The octagonal and circular arrays provided balanced performance across all metrics, offering versatile options for various operational needs. These results provide valuable guidance for optimizing phased array designs to meet specific requirements in Ku-band applications. Tecno Scientifica Publishing 2024-11-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/502 10.53623/gisa.v4i2.502 Green Intelligent Systems and Applications; Volume 4 - Issue 2 - 2024; 80-88 2809-1116 10.53623/gisa.v4i2 eng https://tecnoscientifica.com/journal/gisa/article/view/502/261 Copyright (c) 2024 Muhammad Athallah Adriansyah, Aditya Inzani Wahdiyat, Catur Apriono https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/522 2024-12-12T00:58:50Z gisa:ART

Artificial Neural Network for Benchmarking the Dimensional Accuracy of the PLA Fused Flament Fabrication Process Setiawan, Kevin Stephen Tanaji, Irvantara Pradmaputra Permana, Ari Akbar, Hafizh Naufaly Prihatmaja, Dhonadio Aurell Azhar Normasari, Nur Mayke Eka Rifai, Achmad Pratama Pamungkasari, Panca Dewi Fused Deposition Modeling Artificial Neural Network Scaled Conjugate Gradient Bayesian Regularization Levenberg-Marquardt Fused Deposition Modeling (FDM) is an additive manufacturing technique that uses a 3D printer to extrude molten filament through a nozzle, which moves along the X, Y, and Z axes to create parts with the desired geometry. FDM offers numerous advantages, especially for producing parts with complex shapes, due to its ability to enable rapid and cost-effective manufacturing compared to traditional methods. This study implemented an Artificial Neural Network (ANN) to optimize process parameters aimed at minimizing dimensional inaccuracies in the FDM process. Key parameters considered for optimization included the number of shells, infill percentage, and nozzle temperature. The ANN utilized three algorithms: Scaled Conjugate Gradient, Bayesian Regularization, and Levenberg-Marquardt. Model performance was evaluated based on dimensional deviations along the X and Y axes, with a hidden layer of 25 neurons. Among the algorithms, Scaled Conjugate Gradient provided the most accurate results in minimizing dimensional errors. Tecno Scientifica Publishing 2024-11-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/522 10.53623/gisa.v4i2.522 Green Intelligent Systems and Applications; Volume 4 - Issue 2 - 2024; 89-97 2809-1116 10.53623/gisa.v4i2 eng https://tecnoscientifica.com/journal/gisa/article/view/522/264 Copyright (c) 2024 Kevin Stephen Setiawan, Irvantara Pradmaputra Tanaji, Ari Permana, Hafizh Naufaly Akbar, Dhonadio Aurell Azhar Prihatmaja, Nur Mayke Eka Normasari, Achmad Pratama Rifai, Panca Dewi Pamungkasari https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/526 2024-12-12T01:04:41Z gisa:Review

Literature Review: Biomedical Information of Animal Treadmill Speed Control Using Proportional Integral Derivative Controller Nurbadriani, Cut Nanda Melinda, Melinda Roslidar, Roslidar Heart rat treadmill DC Motor Encoder Sensor PID Control The use of treadmill exercise in cardiovascular research played a vital role in assessing heart health and determining appropriate exercise regimens for patients. Before applying these regimens to humans, experiments on animals, such as white rats or mice, were conducted to simulate human cardiovascular responses. A specialized treadmill designed for experimental animals was required to determine exercise doses based on individual abilities. This process involved controlling the treadmill speed, which was generated by a conveyor driven by a DC motor. The motor speed was regulated through PID (Proportional Integral Derivative) control, while encoder sensors monitored the motor’s rotation speed, and limit switch sensors determined the exercise duration. This article reviewed the design and implementation of treadmill systems used for animal-based cardiovascular research, focusing on the control of DC motor speed using PID controllers. Previous studies that contributed to the development of such systems were discussed, with an emphasis on the precise control mechanisms required to simulate exercise conditions tailored to the subject's abilities. The treadmill system also incorporated sensors to accurately adjust motor speed and track exercise duration, ensuring alignment with the physiological capabilities of the test subjects. Furthermore, this review explored the potential for advancing research on treadmill control systems, offering insights into how this technology could support medical experts in determining optimal exercise regimens for white rats. These developments helped bridge the gap between animal-based studies and human applications, facilitating improved cardiovascular research outcomes. Tecno Scientifica Publishing 2024-12-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/526 10.53623/gisa.v4i2.526 Green Intelligent Systems and Applications; Volume 4 - Issue 2 - 2024; 109-119 2809-1116 10.53623/gisa.v4i2 eng https://tecnoscientifica.com/journal/gisa/article/view/526/274 Copyright (c) 2024 Cut Nanda Nurbadriani, Melinda Melinda, Roslidar Roslidar https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/527 2025-06-23T03:04:36Z gisa:ART

Harnessing Smart Farming: Key Determinants of Automated Mini Greenhouse Adoption and Use in the Philippines Zhuo, Eugenia R. automated mini-greenhouse technology adoption unified theory of acceptance and use of technology 2 (UTAUT 2) behavioral intention smart agriculture This research investigated the determinants of adopting and sustaining the utilization of automated mini-greenhouses in the Philippines, a nation particularly vulnerable to climate change. Using an integrated theoretical framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT2), Diffusion of Innovation (DOI), and Actor-Network Theory (ANT), this research employed a quantitative approach to assess key constructs, such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, trust, habit, and technology readiness. Data were collected through structured surveys administered to smallholder farmers, and the results were analyzed using Python-based statistical tools. The findings indicated that performance expectancy and social influence were significant predictors of technology adoption, while habit and facilitating conditions strongly influenced continued use. Trust and resource accessibility, derived from DOI and ANT, also emerged as critical factors in sustained utilization. These results contributed to understanding smart farming adoption in the context of climate resilience and sustainable agriculture. Future research should explore broader applications of such technologies and further examine their long-term sustainability. Tecno Scientifica Publishing 2025-02-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/527 10.53623/gisa.v5i1.527 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 14-25 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/527/283 Copyright (c) 2025 Eugenia R. Zhuo https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/528 2024-12-12T01:04:43Z gisa:ART

Design of IoT-Based Battery Monitoring for DC Backup Yunidar, Yunidar Fathurrahman, Fathurrahman Melinda, Melinda Azra, Ery Malahayati, M. Elizar, Elizar The battery monitoring process for the DC backup power supply at the Banda Aceh Main Substation was previously performed manually using a multimeter, leading to inefficiencies. This study aimed to develop an automated battery monitoring system based on the Internet of Things (IoT) to enhance operational efficiency. The proposed system integrated a DC voltage sensor (voltage divider) connected to the battery and an INA219 sensor to measure current flow during battery usage. A NodeMCU ESP8266 microcontroller, programmed with the Arduino IDE, served as the main data processor and internet interface. Monitoring data was transmitted to officers via an IoT-based cloud server on the Blynk platform. The system was tested using eight NiCd 1.2 V battery cells arranged to simulate the substation setup. The resulting prototype automated daily battery monitoring, significantly improving the efficiency and effectiveness of the monitoring process. Tecno Scientifica Publishing 2024-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/528 10.53623/gisa.v4i2.528 Green Intelligent Systems and Applications; Volume 4 - Issue 2 - 2024; 98‒108 2809-1116 10.53623/gisa.v4i2 eng https://tecnoscientifica.com/journal/gisa/article/view/528/273 Copyright (c) 2024 Yunidar Yunidar, Fathurrahman Fathurrahman, Melinda Melinda, Ery Azra, M. Malahayati, Elizar Elizar https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/551 2025-06-23T03:04:36Z gisa:ART

Spam and Phishing Whatsapp Message Filtering Application Using TF - IDF and Machine Learning Methods Manurung, Ferdinand Aprillian Munawir Pradeka, Deden The rapid development of communication technology has led to an increase in the number of unwanted messages, such as spam and phishing attempts. However, this progress has not been accompanied by sufficient user awareness of the basics of technology use. Additionally, the enforcement of laws regarding internet-based crimes remains unclear, further increasing the risk for users of internet technology to fall victim to such crimes. As one of the media prone to spam and phishing, WhatsApp is the focus of this research, which aims to develop an application capable of filtering spam and phishing messages. The application employs the TF-IDF (Term Frequency-Inverse Document Frequency) method and machine learning using the Random Forest model. It is developed using the MVVM (Model-View-ViewModel) architecture, enabling the separation of business logic from the user interface, thereby improving development and maintenance efficiency. The research findings demonstrate that the combination of TF-IDF and Random Forest achieves high accuracy in classifying spam and phishing messages. Performance evaluation using a confusion matrix reveals an accuracy rate of 92%. For the safe message class, the precision, recall, and F1 scores are 89%, 95%, and 92%, respectively, while for the dangerous message class, the scores are 95%, 88%, and 92%, respectively. Furthermore, the integration of the model and application performed exceptionally well, as evidenced by black-box testing results. All test scenarios were met, successfully detecting test messages with 98% accuracy. Therefore, the developed application provides enhanced protection for WhatsApp users against digital threats. Tecno Scientifica Publishing 2025-01-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/551 10.53623/gisa.v5i1.551 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 1-13 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/551/280 Copyright (c) 2025 Ferdinand Aprillian Manurung, Munawir, Deden Pradeka https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/552 2025-06-23T03:04:36Z gisa:ART

Fraud Classification in Online Payments Using Supervised Machine Learning Algorithms Editya, Arda Surya Alamin, Moch. Machlul Pramana, Anggay Lury Kurniati, Neny Fraud Classification Machine Learning Online payment systems have become a cornerstone of modern financial transactions, providing convenience and efficiency. However, the rise of such systems has also led to an increase in fraudulent activities, posing significant risks to users and service providers. This research focused on optimizing the classification of fraudulent transactions in online payment systems using supervised machine learning algorithms. This study explored the performance of several widely used algorithms, including Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting Tree, and Support Vector Machine (SVM). A comprehensive dataset of online payment transactions was used to evaluate the effectiveness of these algorithms in identifying fraudulent activities. Various performance metrics, such as accuracy, precision, and F1 score, were employed to assess and compare classification capabilities. In addition, feature engineering and data preprocessing techniques were applied to improve the models’ predictive performance. The results demonstrated that, while each algorithm had its strengths, ensemble-based methods like Gradient Boosting Tree outperformed others in terms of classification accuracy and robustness. The findings highlighted the importance of selecting appropriate machine learning algorithms and fine-tuning their parameters to achieve optimal fraud detection in online payment systems. This study provides valuable insights for financial institutions and developers to enhance security measures and mitigate fraud risks in digital payment platforms. Tecno Scientifica Publishing 2025-03-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/552 10.53623/gisa.v5i1.552 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 40-50 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/552/296 Copyright (c) 2025 Arda Surya Editya, Moch. Machlul Alamin, Anggay Lury Pramana, Neny Kurniati https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/581 2025-06-23T03:04:36Z gisa:ART

Classification of Metal Surface Defects Using Convolutional Neural Networks (CNN) Pratama, Dhika Wahyu Ismail, Muchammad Nurraudah, Restu Rifai, Achmad Pratama Nguyen , Huu Tho Metal Surface Inspection CNN MobileNetV2 K3 Model InceptionV3 Augmentation Metal surface quality inspection is an important step in ensuring that products meet predetermined industry standards. The manual methods used were often slow and prone to errors, so more efficient solutions were needed. The application of Machine Learning (ML)-based technologies, especially Convolutional Neural Networks (CNN), offered an innovative approach to overcome these challenges. CNN had the ability to automatically extract visual features from images with high accuracy, making it an effective tool in defect classification. This research used several CNN architectures, including MobileNetV2 and InceptionV3, as well as a model developed in-house, the K3 Model. Data augmentation, such as rotation and lighting adjustments, was applied to increase variation in the dataset and aid the model in generalization. The research results showed that the K3+Augmentation model achieved the highest accuracy of 100% in testing, with a very low loss of 0.0009. While MobileNetV2 offered better training speed, K3+Augmentation showed superior performance in detecting and classifying metal defects. These findings confirmed the potential of CNN in improving the efficiency of quality inspection in modern industry. Tecno Scientifica Publishing 2025-05-03 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/581 10.53623/gisa.v5i1.581 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 93-105 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/581/303 Copyright (c) 2025 Dhika Wahyu Pratama, Muchammad Ismail, Restu Nurraudah, Achmad Pratama Rifai, Huu Tho Nguyen https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/588 2025-06-23T03:04:36Z gisa:ART

Twitter Sentiment Analysis of Mental Health Issues Post COVID-19 Pamungkasari, Panca Dewi Ningsih, Sari Rifai, Achmad Pratama Nandila, Alisyafira Sayyidina Nguyen, Huu Tho Penchala, Sathish Kumar Mental Health COVID-19 Sentiment Analysis Twitter Na¨ıve Bayes Support Vector Machine The Coronavirus Disease 2019 (COVID-19) impacted many aspects of daily life, including mental health, as some individuals struggled to adjust to the rapid changes brought on by the pandemic. This paper investigated sentiment analysis of Twitter data following the COVID-19 pandemic. Specifically, we analyzed a large corpus of tweets to understand public sentiment and its implications for mental health in the post-pandemic context. The Naïve Bayes and Support Vector Machine (SVM) classifiers were used to categorize tweets into positive, negative, and neutral sentiments. The collected tweet data samples showed that 38.35% were neutral, 32.56% were positive, and 29.09% were negative. Results using the SVM method showed an accuracy of 84%, while Naïve Bayes achieved 80% accuracy. Tecno Scientifica Publishing 2025-03-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/588 10.53623/gisa.v5i1.588 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 51-60 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/588/297 Copyright (c) 2025 Panca Dewi Pamungkasari, Sari Ningsih, Achmad Pratama Rifai, Alisyafira Sayyidina Nandila, Huu Tho Nguyen, Sathish Kumar Penchala https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/592 2025-06-23T03:04:36Z gisa:ART

Design of A Braille Printer Based on ESP32 Microcontroller with Voice Input Beatrix, Maria Wahab, Wahidin Wulandari, Meirista Braille is a tactile phonetic alphabet system invented by Louis Braille, a blind teacher from France, in the 1800s. The Braille system was recognized as "a vital language of communication, as valid as all other languages in the world" in 2005. There are other alternatives, such as touch-based methods, to convey information that is generally obtained through sight. One of them is the use of Braille letters for reading, writing, and improving welfare by increasing insight. However, only 52 special schools in Indonesia have printers for Braille books. Limited access to Braille printing facilities in Indonesia is due to high costs. The cost of a printer machine, approximately 50 million per school, poses a challenge in providing learning facilities. This research proposes a compact and cost-effective Braille printer using an ESP32 microcontroller with both speech and mechanical switch inputs. The mechanical switch is used for typing text to be printed, while the microphone captures sound input in the form of audio, as it is easier to use. Audio input is processed using speech-to-text technology. The speech-to-text process is carried out with speech recognition, which listens to the words spoken by the user and matches them with the data in the module to execute specific commands. This Braille printer is designed to print Braille letters based on data received directly from individuals with and without disabilities. The printer accepts input in the form of speech or text, which is then sent to the processing module, the ESP32 microcontroller. Once all data is processed, the Braille printer module controls axis movements using a stepper motor. Braille prints are embossed to create raised dots on paper. Experimental results demonstrate 100% accuracy for both speech and typing inputs, along with reliable printing performance on standard HVS paper. Compared to previous solutions, the proposed design is more versatile, affordable, and portable. This study presents a practical solution for increasing access to Braille education and information. Tecno Scientifica Publishing 2025-02-26 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/592 10.53623/gisa.v5i1.592 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 26-39 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/592/287 Copyright (c) 2025 Maria Beatrix, Wahidin Wahab, Meirista Wulandari https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/605 2025-06-23T03:04:36Z gisa:ART

Comparison Of Feature Extraction Techniques For Long Short-Term Memory Models In Indonesian Automatic Speech Recognition Armaisya, Dimas Dwi Pamungkasari, Panca Dewi Rifai, Achmad Pratama Sholihati, Ira Diana Gopal Sakarkar Automatic Speech Recognition (ASR) faced challenges in accuracy and noise robustness, particularly in Bahasa Indonesia. This research addressed the limitations of single feature extraction methods, such as Mel-Frequency Cepstral Coefficients (MFCC), which were sensitive to noise, and Relative Spectral Transform - Perceptual Linear Predictive (RASTA-PLP), which was less effective in frequency representation, by proposing a hybrid approach that combined both techniques using Long Short-Term Memory (LSTM) models. MFCC enhanced spectral accuracy, while RASTA-PLP improved noise robustness, resulting in a more adaptive and informative acoustic representation. The evaluation demonstrated that the hybrid method outperformed single and non-extraction approaches, achieving a Character Error Rate (CER) of 0.5245 on clean data and 0.8811 on noisy data, as well as a Word Error Rate (WER) of 0.9229 on clean data and 1.0015 on noisy data. Although the hybrid approach required longer training times and higher memory usage, it remained stable and effective in reducing transcription errors. These findings suggested that the hybrid method was an optimal solution for Indonesian speech recognition in various acoustic conditions. Tecno Scientifica Publishing 2025-04-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/605 10.53623/gisa.v5i1.605 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 74-92 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/605/299 Copyright (c) 2025 Dimas Dwi Armaisya, Panca Dewi Pamungkasari, Achmad Pratama Rifai, Ira Diana Sholihati, Gopal Sakarkar https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/606 2025-06-23T03:04:36Z gisa:ART

Land Subsidence Analysis Using Machine Learning Algorithm Random Forest Method in DKI Jakarta Nur Hidayah, Camelia Pamungkasari, Panca Dewi Ningsih, Sari Azhiman, Muhammad Fauzan Widodo, Joko Widayaka, Elfady Satya Random Forest DKI Jakarta Land Subsidence PS-InSAR Land subsidence is an environmental phenomenon that causes the earth's surface to decline gradually or suddenly. Land subsidence occurred in DKI Jakarta due to various factors such as excessive groundwater exploitation, infrastructure loads, and geological conditions. The purpose of this study was to analyze land subsidence in DKI Jakarta and the distribution of existing land subsidence. The results were compared with previous findings using PS-InSAR. Land subsidence was predicted using the Random Forest algorithm. Random Forest, as a type of machine learning, was able to reduce noise and minimize the impact of overfitting through ensemble techniques. Researchers used four metrics, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R², and Kling-Gupta Efficiency (KGE), to assess the accuracy of the algorithm. The analysis results of land subsidence in DKI Jakarta using Random Forest aligned with the PS-InSAR method. It was observed that areas experiencing land subsidence were predominantly in North and West Jakarta compared to other regions. Furthermore, the prediction of land subsidence using the 2017–2021 dataset indicated a decrease of up to -60 mm/year. Tecno Scientifica Publishing 2025-05-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/606 10.53623/gisa.v5i1.606 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 106-122 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/606/315 Copyright (c) 2025 Camelia Nur Hidayah, Panca Dewi Pamungkasari, Sari Ningsih, Muhammad Fauzan Azhiman, Joko Widodo, Elfady Satya Widayaka https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/607 2025-06-23T03:04:36Z gisa:ART

A Benchmark Study of DeepLabV3+, U-Net++, and Attention U-Net for Blood Cell Segmentation Angelina, Clara Lavita Rospawan, Ali Cell segmentation DeepLabV3 U-Net Attention U-Net Biomedical image analysis Blood cell Cell segmentation is a critical process in biomedical image analysis. This study evaluated the performance of three state-of-the-art deep learning models—DeepLabV3+, U-Net++, and Attention U-Net—using the Blood Cell Count and Detection (BCCD) dataset, which contains annotated images of blood cells. The models were rigorously analyzed through qualitative and quantitative evaluations, employing accuracy, precision, recall, and F1 score metrics. The results demonstrated that all three models achieved high segmentation performance, with U-Net++ excelling in accuracy (0.9740), precision (0.9511), and F1 score (0.9576), Attention U-Net achieving the highest recall (0.9692), and DeepLabV3+ providing a balanced performance across all metrics. Qualitative analyses revealed that U-Net++ delivered superior segmentation of complex and overlapping cell structures, while Attention U-Net exhibited exceptional sensitivity to dense cell clusters. Training and validation curves of the models confirmed their stability and generalizability, indicating efficient convergence without overfitting. By highlighting the unique strengths of each model, this study emphasized the importance of selecting architectures tailored to specific tasks. Future research will expand the application of these models to diverse biomedical datasets to further advance automated image analysis and its impact on healthcare outcomes. Tecno Scientifica Publishing 2025-03-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/607 10.53623/gisa.v5i1.607 Green Intelligent Systems and Applications; Volume 5 - Issue 1 - 2025; 61-73 2809-1116 10.53623/gisa.v5i1 eng https://tecnoscientifica.com/journal/gisa/article/view/607/298 Copyright (c) 2025 Clara Lavita Angelina, Ali Rospawan https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/706 2025-12-03T06:52:37Z gisa:Review

A Systematic Literature Review of YOLO and IoT Applications in Smart Waste Management Gelar, Trisna Fitriani, Sofy Rachmat, Setiadi IoT Object Detection SLR Waste Management YOLO The increase in urbanization and global population expansion resulted in increased garbage production, causing considerable environmental and public health issues that exceeded traditional waste management approaches. To tackle these challenges, automated waste detection and analysis integrated computer vision, especially deep learning, with the Internet of Things (IoT) in intelligent waste management applications. This comprehensive literature review investigated a wide range of You Only Look Once (YOLO) applications in IoT-based waste detection and management, demonstrating its efficacy in addressing global waste issues. Employing specific keywords and Boolean operators, the review followed a rigorous methodology to explore reputable electronic databases for peer-reviewed articles published from 2019 to 2025. The primary findings indicated that different iterations of YOLO (v3 to v12) were integrated with diverse IoT devices and computing setups, including edge and centralized systems. These integrations facilitated four crucial applications: hazardous waste management, monitoring of smart bins, classification of waste types, and detection of litter in public spaces. This integration enhanced sustainability through improved waste management practices, increased efficiency in waste processes, and reduced manual labor requirements. Challenges included precise waste identification in complex scenarios, adaptation to fluctuating environmental conditions, and ensuring dependable, low-power operation of IoT devices. To sum up, the integration of YOLO and IoT established a robust basis for intelligent waste management, transforming reactive approaches into proactive strategies. Moving forward, research should prioritize enhancing the integration and power management of IoT sensors, optimizing edge deployment, and developing more resilient YOLO models. Tecno Scientifica Publishing 2025-08-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/706 10.53623/gisa.v5i2.706 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 123-139 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/706/347 Copyright (c) 2025 Trisna Gelar, Sofy Fitriani, Setiadi Rachmat https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/761 2025-12-03T06:52:37Z gisa:ART

Design and Build a Push-Pull Inverter for Room Lighting Haryanti, Munnik Yulianti, Bekti Rahmawati, Cynthia Adhicandra, Iwan push pull inverter solar panel PWM engineering This study addressed the issue of harmonic distortion in solar power systems that required inverters to convert DC voltage to AC for indoor lighting applications. The objective was to design and evaluate a push-pull inverter incorporating pulse width modulation (PWM) to reduce harmonics and ensure a stable voltage output. A push-pull topology was selected because of its relatively simple design and ability to step up DC voltage using a transformer, making it suitable for low- to medium-power applications. The inverter employed two metal–oxide–semiconductor field-effect transistor (MOSFET) switching devices operated alternately to generate AC waves at the output. The core of the design was a 50 Hz pulse generator producing a 5 V pulse signal with a small current, which was then amplified using a current amplifier before being supplied to the transformer. The transformer functioned to induce the electromagnetic field from the pulse source and release it at a higher voltage of 220 V. Experimental testing was performed using 2.3 W, 5 W, and 8 W LED lights. A minor modification to the gate resistor improved system performance, resulting in stable transformer output voltages at 5 W and 8 W loads. These results demonstrated that the PWM-controlled push-pull inverter successfully reduced harmonics and maintained voltage stability under higher loads, making it effective for indoor LED lighting powered by solar energy. Future studies could aim to enhance efficiency at lower loads, minimize switching losses, and implement more advanced PWM techniques to achieve performance levels comparable to pure sine wave inverters. Tecno Scientifica Publishing 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/761 10.53623/gisa.v5i2.761 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 140-149 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/761/348 Copyright (c) 2025 Munnik Haryanti, Bekti Yulianti, Cynthia Rahmawati, Iwan Adhicandra https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/782 2025-12-03T06:52:37Z gisa:ART

Implementation of Key Performance Indicators in the Palm Oil Harvest Monitoring Information System Rina Sari, Diah Ayu Irawan, Muhammad Dedi Key Performance Indicator information system harvest result palm oil monitoring Manual management of palm oil harvest data often resulted in data inconsistencies, low operational efficiency, and financial losses for plantation companies. To address these challenges, this study aimed to implement the Key Performance Indicator (KPI) method within a harvest monitoring information system at PT Perkebunan Nusantara (PTPN; Nusantara Plantation Company, Ltd) IV Regional II Unit Adolina, with the goal of enhancing data accuracy, transparency, and managerial effectiveness. A quantitative research approach was employed, utilizing data collection techniques such as observation, interviews, and document analysis. Five out of ten afdeling units were selected as research samples. The performance was assessed using three primary indicators: total harvest yield (weighted at 40%), labour productivity (35%), and monthly harvest frequency (25%). The results revealed significant variations in KPI achievement across afdeling units, with several units exceeding the established targets. Furthermore, the developed system featured an interactive visual dashboard that enabled managers to monitor performance in real time and supported data-driven decision-making. Compared to traditional monitoring tools, this system offered enhanced integration of performance metrics, automated data processing, and real-time analytics, addressing previous limitations such as delayed reporting and fragmented data sources. In conclusion, the integration of KPI into the harvest monitoring information system proved to be effective in providing objective and measurable performance evaluation. This approach offered a strategic solution for improving operational efficiency and productivity in palm oil plantation management. Tecno Scientifica Publishing 2025-08-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/782 10.53623/gisa.v5i2.782 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 150-163 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/782/349 Copyright (c) 2025 Diah Ayu Rina Sari, Muhammad Dedi Irawan https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/880 2025-12-25T01:29:25Z gisa:ART

Temporal Analysis of Land Subsidence in DKI Jakarta Using the Long Short-Term Memory (LSTM) Model Fitriany, Heni Pamungkasari, Panca Dewi Wijaya, Yunan Fauzi Wijaya Azhiman, Muhammad Fauzan Nagase, Yasuhito Nagase Widodo, Joko land subsidence LSTM PS-InSAR Deep Learning temporal prediction This research investigated temporal patterns of land subsidence in DKI Jakarta by applying a Long Short-Term Memory (LSTM) model to deformation measurements derived from Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) observations acquired between 2017 and 2021. Because the original PS-InSAR time series was characterized by uneven acquisition intervals, the deformation records were first resampled into a uniform 11-day sequence to obtain a consistent temporal structure for modeling. Preprocessing steps, comprising outlier handling, temporal resampling, and feature normalization, were performed to ensure that the model could capture deformation behavior reliably. The LSTM architecture employed three stacked recurrent layers and was trained using the Adam optimizer with Smooth L1 Loss and an early-stopping strategy. Model evaluation demonstrated excellent agreement between predicted and observed deformation, yielding R² = 1.000, MSE = 0.104, RMSE = 0.322 mm, and KGE = 0.998. Compared with a previously developed Random Forest model (R² = 0.9995, RMSE = 0.3314 mm), the LSTM approach exhibited more stable temporal learning and was better suited for long-horizon deformation forecasting. Spatial analysis revealed that northern Jakarta, particularly Cengkareng, Tanjung Priok, and Pantai Indah Kapuk, continued to experience the greatest cumulative subsidence (> −30 mm), whereas areas in the south, such as Jagakarsa and Kebayoran Baru, showed minimal deformation (< −5 mm), aligning with known geological and anthropogenic conditions. Overall, integrating PS-InSAR time series with an LSTM framework provided a more coherent and temporally consistent method for characterizing subsidence behavior in Jakarta. The outcomes of this study offered a scientific basis for developing intelligent monitoring tools to support mitigation efforts and sustainable urban planning in regions affected by land subsidence. Tecno Scientifica Publishing 2025-12-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/880 10.53623/gisa.v5i2.880 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 164-180 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/880/404 Copyright (c) 2025 Heni Fitriany, Panca Dewi Pamungkasari, Yunan Fauzi Wijaya Wijaya, Muhammad Fauzan Azhiman, Yasuhito Nagase Nagase, Joko Widodo https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/899 2026-03-05T04:26:31Z gisa:ART

Comparison of Convolutional Neural Network Model for Brain Tumor Disease Gliome Detection Santoso, Wulan Sallyndri Saragih, Riko Arlando Glioma represented one of the most aggressive forms of malignant brain tumors, necessitating early detection to optimize therapeutic intervention outcomes. Manual tumor identification through Magnetic Resonance Imaging (MRI) was labor-intensive and was susceptible to subjective interpretation errors. This study aimed to compare the performance of two Convolutional Neural Network (CNN) architectures, specifically Residual Network (ResNet) and U-Net, for glioma tumor detection in T2-weighted MRI sequences. The datasets employed were obtained from the BraTS and Kaggle repositories and underwent comprehensive preprocessing procedures, including normalization, augmentation, and conversion to Portable Network Graphics (PNG) format. The evaluation metrics demonstrated that the U-Net architecture exhibited superior performance compared to ResNet-18, achieving an accuracy of 88.16%, sensitivity of 80.00%, specificity of 88.43%, and F1-score of 68.97%. Conversely, ResNet-18 yielded an accuracy of 71.43%, sensitivity of 73.52%, specificity of 81.54%, and an F1-score of 70.14%. These findings indicated that U-Net demonstrated greater efficacy in recognizing tumor morphology within MRI data and preserving spatial information through its inherent skip connection mechanism. This investigation demonstrated the potential of the U-Net architecture to facilitate automated and enhanced accuracy in glioma detection, although further refinement was required to improve segmentation precision and clinical applicability. Tecno Scientifica Publishing 2026-03-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/899 10.53623/gisa.v6i1.899 Green Intelligent Systems and Applications; Volume 6 - Issue 1 - 2026; 51−64 2809-1116 10.53623/gisa.v6i1 eng https://tecnoscientifica.com/journal/gisa/article/view/899/463 Copyright (c) 2026 Wulan Sallyndri Santoso, Riko Arlando Saragih https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/910 2025-12-25T01:29:24Z gisa:ART

Design an Electricity Consumption Prediction Information System Using the Monte Carlo-Based Regression Tree Method Ng, Junira Merrylin Hoendarto, Genrawan Willay, Thommy Electricity consumption prediction Monte Carlo simulation regression tree Internet of Things web-based information system Electricity became an essential component in every industry and was widely used in organizations and households. Improper handling of electricity consumption resulted in unnecessary energy loss and increased costs. The objective of this study was to develop an online electricity consumption prediction information system that was efficient, reliable, and capable of rapid forecasting. The system used IoT sensor data from Universitas Widya Dharma Pontianak, and the Monte Carlo based Regression Tree (MCRT) method was employed to mitigate the unpredictability of the data. Feature selection was conducted using Monte Carlo simulation to identify the most important features, which in this case were the year, month, and day, and these were used in the regression tree model. The developed system was able to provide estimations of hourly and daily energy consumption and the associated costs based on the MCRT model. The MCRT model predicted daily energy consumption with an accuracy of 91.61%, outperforming the Monte Carlo simulation (85.39%) and the Regression Tree method (84.29%). The results demonstrated that the MCRT model was the most efficient in capturing non-linear relationships and regression patterns in the energy consumption data. The constructed system featured an easy-to-use web interface that captured real-time data inputs and visualized predicted consumption for operational use. The system was suitable for public and private sectors, as well as educational and household applications. This approach improved effectiveness in energy management and streamlined resource allocation decision-making. The study highlighted the potential of integrating the Internet of Things (IoT) with predictive analytics to provide actionable, reliable, and precise energy management and monitoring services. Tecno Scientifica Publishing 2025-12-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/910 10.53623/gisa.v5i2.910 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 181-190 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/910/405 Copyright (c) 2025 Junira Merrylin Ng, Genrawan Hoendarto, Thommy Willay https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/919 2026-03-14T05:29:20Z gisa:ART

Recency, Frequency, and Monetary-Based Customer Segmentation Using K-Means for Analysing Transactional Behaviour in a Service-Based Micro, Small, and Medium Enterprises Ardiansyah, Rizka Trezandy, Nouval skandar, Iskandar Ilman, Meilani Sahril, Sahril RFM K-Means Customer Segmentation MSME Laundry Service Data-Driven Marketing Micro, Small, and Medium Enterprises (MSMEs) often faced challenges in designing effective promotional initiatives due to the limited use of systematic customer behavior analysis. This study examined the application of (Recency, Frequency, Monetary) RFM analysis combined with K-Means clustering to explore customer segmentation in a service-based MSME context. Transaction data from a local laundry service operating in Palu, Indonesia, consisting of 2,220 digital transaction records collected between 2022 and 2025, were processed and transformed into RFM variables using min–max normalization. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments. Cluster quality was evaluated using internal validation metrics, yielding a Davies–Bouldin Index (DBI) of 0.61 and a Sum of Squared Errors (SSE) value of 1.73, indicating reasonably compact and well-separated clusters. The resulting segments exhibited distinct transactional profiles across recency, transaction frequency, and monetary contribution, reflecting heterogeneity in customer engagement within the studied MSME. Rather than prescribing specific marketing actions, the findings provided an interpretable analytical basis for considering differentiated promotional strategies aligned with observed customer behavior patterns. Overall, this study demonstrated that RFM-based segmentation offered a feasible and data-driven approach to supporting evidence-informed promotional planning in service-oriented MSMEs operating under data and resource constraints. Tecno Scientifica Publishing 2026-03-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/919 10.53623/gisa.v6i1.919 Green Intelligent Systems and Applications; Volume 6 - Issue 1 - 2026; 65−80 2809-1116 10.53623/gisa.v6i1 eng https://tecnoscientifica.com/journal/gisa/article/view/919/472 Copyright (c) 2026 Rizka Ardiansyah, Nouval Trezandy, Iskandar skandar, Meilani Ilman, Sahril Sahril https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/930 2025-12-25T01:29:22Z gisa:ART

Maritime Cybersecurity Readiness and Training Standards in Indonesia Rakka, Stevian G. A. Gumilar, Bagja Wijaya, Haryadi Ansar, Azhar Ariansyah Cybersecurity Readiness Maritime Training IoT-Enabled Systems Cyber-Physical Systems The rapid digitalization of maritime operations through IoT-enabled navigation systems and cyber-physical ship infrastructures increased Indonesia’s exposure to cybersecurity risks. Strengthening cybersecurity competence within Maritime Education and Training (MET) institutions was therefore essential to ensure navigational safety, operational reliability, and national maritime resilience. This study assessed cybersecurity readiness, training standards, instructor competence, and facility availability in Indonesian MET institutions with reference to international frameworks, including IMO MSC-FAL.1/Circ.3, BIMCO guidelines, and ISO/IEC 27001. A descriptive quantitative approach was employed using structured questionnaires to evaluate organizational readiness, curriculum implementation, instructor qualifications, and supporting facilities. Data were analyzed using percentage distributions to identify institutional conditions and gaps relative to global requirements. The results indicated that cybersecurity training in most MET institutions remained largely theoretical, with limited practical exposure. Nearly 80% of respondents reported having no prior cybersecurity training, while hands-on facilities such as cyber laboratories and simulation environments were largely unavailable. Instructor expertise and standardized cybersecurity modules aligned with international guidelines were insufficient to adequately address threats to AIS, GPS, ECDIS, and integrated IT–OT systems. These findings revealed a significant gap between existing training practices and the competencies required for secure digital maritime operations. The study concluded that standardized, practice-oriented cybersecurity training was urgently needed, supported by instructor upskilling, curriculum alignment with international standards, and the development of shared training facilities. Strengthening these aspects was critical to improving national maritime cyber readiness and supporting resilient intelligent maritime systems. Tecno Scientifica Publishing 2025-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/930 10.53623/gisa.v5i2.930 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 205−216 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/930/415 Copyright (c) 2025 Stevian G. A. Rakka, Bagja Gumilar, Haryadi Wijaya, Azhar Ariansyah Ansar https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/934 2025-12-25T01:29:21Z gisa:ART

Implementation of the You Look Only Once (YOLOv11) Algorithm to Detect the Ripeness of Golden Melons Tandoballa, Lucky Hartati, Ery adam optimizer; melon ripness detection; object detection; YOLOv11 Melon is a horticultural commodity with high economic value, and characteristics such as sweetness, aroma, texture, and phytonutrient content significantly influenced consumer preference. Conventional methods for determining melon ripeness were time-consuming, required considerable expertise, and were often prone to subjective errors, ultimately slowing the production and distribution process. This study aimed to detect the ripeness level of golden melon fruit non-destructively using the YOLOv11 algorithm, focusing on external physical characteristics as the basis for classification. The objectives included applying transfer learning to categorize golden melon into ripe and unripe classes and evaluating model performance using precision, recall, mAP50, mAP50-95, and F1-score. The research methodology consisted of a literature review, dataset collection from previous studies, system design, implementation, and performance testing. The dataset was divided into 70% training, 20% validation, and 10% testing data, and the Adam optimizer was used during the training phase. Based on four experimental scenarios, scenario 3 produced the best and most consistent results, achieving a precision of 90.58%, a recall of 90.79%, an mAP50 of 97.31%, an mAP50-95 of 88.84%, and an F1-score of 92.97%. These findings demonstrated that scenario 3 offered optimal performance for detecting golden melon ripeness. Thus, the model was highly reliable overall. Tecno Scientifica Publishing 2025-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/934 10.53623/gisa.v5i2.934 Green Intelligent Systems and Applications; Volume 5 - Issue 2 - 2025; 191−204 2809-1116 10.53623/gisa.v5i2 eng https://tecnoscientifica.com/journal/gisa/article/view/934/414 Copyright (c) 2025 Lucky Tandoballa, Ery Hartati https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/963 2026-03-05T04:26:35Z gisa:ART

Integrating TOGAF 10 and ISO 20000-1:2018 for Digital Multi-finance Service Level Agreement/Mean Time to Repair improvements Destian, Bagus Resa Pamungkasari, Panca Dewi Digital transformation in the multi-finance sector demands service architectures that are flexible, reliable, and scalable; however, misalignment between architectural design and operational execution often leads to weak service performance. This study proposes an integrated framework that combines TOGAF 10 artifacts with ISO/IEC 20000-1:2018 processes to systematically estimate Service Level Agreement (SLA) targets and reduce Mean Time to Repair (MTTR). Using a Design Science Research approach, the framework was implemented in a 14-month case study at PT XYZ Multi-finance. The resulting artifacts include a bidirectional traceability model linking business objectives to SLA and MTTR indicators, as well as an operability pattern catalog to support “design for operability.” The implementation delivered measurable operational improvements: MTTR decreased from a peak of 775 minutes to below 60 minutes, Mean Time to Detect (MTTD) was reduced by approximately 90%, SLA compliance increased to 99.7%, and incidents caused by manual configuration errors declined. These results demonstrate that integrating enterprise architecture design with service management processes can significantly improve service reliability and overall operational performance. Tecno Scientifica Publishing 2026-02-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/963 10.53623/gisa.v6i1.963 Green Intelligent Systems and Applications; Volume 6 - Issue 1 - 2026; 1-18 2809-1116 10.53623/gisa.v6i1 eng https://tecnoscientifica.com/journal/gisa/article/view/963/444 Copyright (c) 2026 Bagus Resa Destian, Panca Dewi Pamungkasari https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/1009 2026-03-05T04:26:34Z gisa:ART

An Image Processing-Based Fire Detection System Using Orange Pi 4A with Internet of Things Integration in Indoor Environments Pratiwi, Safeti Intan Puji Widiyanto, Eka Fire detection YOLOv11 Orange Pi 4A Computer vision Internet of Things Indoor safety Fire hazards in indoor industrial environments require fast and reliable detection systems, as conventional sensor-based methods often suffer from delayed responses and high false-alarm rates. This study proposes a low-cost, Internet of Things-integrated visual fire detection system based on the YOLOv11 deep learning model implemented on an Orange Pi 4A. The system integrates an IP camera for visual acquisition, real-time detection, and automatic data logging through a MySQL-based monitoring platform. Experiments were conducted in a 3 × 3 m indoor environment using candle, stove, and burning fires at various camera distances. System performance was evaluated using confidence score, bounding box pixel area, and recall based on True Positive and False Negative classifications. Candle flames were reliably detected up to 100 cm with recall values of 90.24%–100% and pixel areas below 5,000 px, while stove flames achieved recall above 93% at 50–100 cm with pixel areas of 11,144–42,525 px. Burning fires maintained high performance up to 300 cm, reaching confidence values above 0.70 and recall rates of 78.94%–100% with pixel areas exceeding 44,000 px. The results indicate that detection reliability is primarily influenced by apparent flame size rather than camera distance. Overall, the proposed system demonstrates strong feasibility as an embedded, IoT-integrated fire detection solution for early warning in indoor industrial environments, although limitations remain in detecting small flames under low-resolution and low-light conditions. Tecno Scientifica Publishing 2026-02-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/1009 10.53623/gisa.v6i1.1009 Green Intelligent Systems and Applications; Volume 6 - Issue 1 - 2026; 19−33 2809-1116 10.53623/gisa.v6i1 eng https://tecnoscientifica.com/journal/gisa/article/view/1009/453 Copyright (c) 2026 Safeti Intan Pratiwi, Eka Puji Widiyanto https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/1012 2026-03-05T04:26:33Z gisa:ART

Design and Implementation of a Multi-Node Gas Sensor-Based Indoor Air Quality Monitoring and Control System Alkan Dawasoka, Siti Milda Puji Widiyanto, Eka :  Air quality monitoring was a crucial aspect of maintaining occupational health and safety, particularly in industrial environments. This study proposed the design and implementation of an Internet of Things (IoT)-based indoor air quality monitoring system capable of measuring environmental parameters in real time. The system integrated an ENS160 gas sensor and an AHT21 temperature–humidity sensor with a Wemos D1 Mini microcontroller. Sensor data were transmitted via the MQTT protocol to an Orange Pi 4A server and visualized using a Node-RED dashboard. The monitored parameters included Total Volatile Organic Compounds (TVOC), equivalent CO₂ (eCO₂), temperature, and humidity. Experimental evaluation demonstrated that the system responded proportionally to different pollutant exposure levels. Under high NH₃ exposure (100%), TVOC values reached a maximum of 12,697 ppb with an average of 5,037 ppb, clearly exceeding the hazardous threshold (>200 ppb). At moderate exposure (50%), the average TVOC decreased to 2,106 ppb, while at low exposure (10%), the average value remained within the safe range at 84 ppb. For eCO₂ testing, cigarette smoke exposure produced a peak value of 11,524 ppm with an average of 1,663 ppm, indicating hazardous conditions (>1000 ppm). Statistical analysis using mean and standard deviation confirmed that sensor stability improved at lower pollutant concentrations. The proposed system successfully provided stable real-time monitoring, threshold-based classification, and automatic mitigation control, demonstrating its feasibility for intelligent indoor air quality management in industrial workspaces. Tecno Scientifica Publishing 2026-02-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/1012 10.53623/gisa.v6i1.1012 Green Intelligent Systems and Applications; Volume 6 - Issue 1 - 2026; 34−50 2809-1116 10.53623/gisa.v6i1 eng https://tecnoscientifica.com/journal/gisa/article/view/1012/454 Copyright (c) 2026 Siti Milda Alkan Dawasoka, Eka Puji Widiyanto https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/1063 2026-03-09T00:45:12Z gisa:ART

Potato Leaf Disease Classification Using MobileNetV3 Architecture With Adam and Stochastic Gradient Descent Optimizers Pebrian, Hafizh Hartati, Ery Potato leaf diseases such as Early Blight and Late Blight reduced productivity and could cause crop failure if they were not detected early. This study analyzed the comparative performance of the Adam and Stochastic Gradient Descent (SGD) optimizers using the MobileNetV3-Large architecture for potato leaf disease classification. The dataset consisted of three categories: healthy leaves, Early Blight, and Late Blight, with a total of 4,072 images. All images were processed through preprocessing stages, including resizing to 224 × 224 pixels and pixel value normalization. The data were divided into training, validation, and testing sets with a ratio of 70:20:10. Random undersampling and data augmentation techniques were applied to the training data to address class imbalance and improve the model’s generalization capability. The model training process was conducted using a transfer learning approach with the MobileNetV3-Large architecture through two stages: feature extraction and fine-tuning. Model performance evaluation was based on accuracy, precision, recall, and F1-score metrics. The results showed that the Adam optimizer achieved a test accuracy of 98.75% with an F1-score of 0.9875, while the SGD optimizer achieved a test accuracy of 96.56% with an F1-score of 0.9635. The Adam optimizer also demonstrated faster and more stable convergence during the training process. This study was expected to serve as a reference for determining an appropriate optimizer for deep learning applications in image classification, particularly in plant disease detection. Tecno Scientifica Publishing 2026-03-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/gisa/article/view/1063 10.53623/gisa.v6i1.1063 Green Intelligent Systems and Applications; Volume 6 - Issue 1 - 2026; 81−95 2809-1116 10.53623/gisa.v6i1 eng https://tecnoscientifica.com/journal/gisa/article/view/1063/477 Copyright (c) 2026 Hafizh Pebrian, Ery Hartati https://creativecommons.org/licenses/by/4.0