2024-03-29T12:02:45Z
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; Vol. 1 Iss. 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; Vol. 1 Iss. 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; Vol. 1 Iss. 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; Vol. 1 Iss. 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; Vol. 1 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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; Vol. 2 Iss. 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
2023-11-18T11:15:45Z
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; Vol. 3 Iss. 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
2023-11-18T11:15:45Z
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; Vol. 3 Iss. 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
2023-11-18T11:15:45Z
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; Vol. 3 Iss. 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
2023-11-18T11:15:45Z
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; Vol. 3 Iss. 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
2023-11-18T11:15:45Z
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; Vol. 3 Iss. 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; Vol. 3 Iss. 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; Vol. 3 Iss. 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; Vol. 3 Iss. 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; Vol. 3 Iss. 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; Vol. 3 Iss. 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
2024-03-18T07:16:40Z
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; Vol. 4 Iss. 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
2024-02-20T10:02:32Z
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; Vol. 4 Iss. 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
2024-02-20T10:02:30Z
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; Vol. 4 Iss. 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