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Green Intelligent Systems and Applications

Journal Description

Green Intelligent Systems and Applications

Green Intelligent Systems and Applications (e-ISSN: 2809-1116) is an international, scientific, peer-reviewed, open-access journal on theoretical and applied sciences related to all aspects of green technologies and intelligent systems published biannually online by Tecno Scientifica.

Open Access
e-ISSN: 2809-1116
Development of COVID-19 Isolation Facility Management System with Scrum Framework
by Sandy Darmowinoto, Syed Rafi Hossain, Puji Astuti

Green Intell. Syst. Appl. 2022, 2(2), pp 96-107;

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. Full text

Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems
by Ali Rospawan, Yukai Yang, Po-Hsu Chen, Ching-Chih Tsai

Green Intell. Syst. Appl. 2022, 2(2), pp 84-95;

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).  Full text

Attendance System with Face Recognition, Body Temperature, and Use of Mask using Multi-Task Cascaded Convolutional Neural Network (MTCNN) Method
by Noor Cholis Basjaruddin, Edi Rakhman, Yana Sudarsa, Moch Bilal Zaenal Asyikin, Septia Permana

Green Intell. Syst. Appl. 2022, 2(2), pp 71-83;

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. Full text

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review
by Aditya Eka Mulyono, Priska Apnitami, Insani Sekar Wangi, Khalfan Nadhief Prayoga Wicaksono, Catur Apriono

Green Intell. Syst. Appl. 2022, 2(2), pp 53-70;

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. Full text

Big Data in Supply Chain Management: A Systematic Literature Review
by Johan Krisnanto Runtuk, Filson Sidjabat, Jsslynn, Felicia Jordan

Green Intell. Syst. Appl. 2022, 2(2), pp 108-117;

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.  Full text