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Green Intelligent Systems and Applications
Published by Tecno Scientifica
ISSN : -     EISSN : 28091116     DOI : https://doi.org/10.53623/gisa.v2i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The journal is intended to provide a platform for research communities from different disciplines to disseminate, exchange and communicate all aspects of green technologies and intelligent systems. The topics of this journal include, but are not limited to: Green communication systems: 5G and 6G communication systems, power harvesting, cognitive radio, cognitive networks, signal processing for communication, delay tolerant networks, smart grid communications, power-line communications, antenna and wave propagation, THz technology. Green computing: high performance cloud computing, computing for sustainability, CPSS, computer vision, distributed computing, software engineering, bioinformatics, semantics web. Cyber security: cryptography, digital forensics, mobile security, cloud security. Internet of Things (IoT): sensors, nanotechnology applications, Agriculture 5.0, Society 5.0. Intelligent systems: artificial intelligence, machine learning, deep learning, big data analytics, neural networks. Smart grid: distributed grid, renewable energy in smart grid, optimized power delivery, artificial intelligence in smart grid, smart grid control and operation.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 22 Documents
 1   2   3 
The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review Aditya Eka Mulyono; Priska Apnitami; Insani Sekar Wangi; Khalfan Nadhief Prayoga Wicaksono; Catur Apriono
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.95

Abstract

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.
Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems Ali Rospawan; Yukai Yang; Po-Hsu Chen; Ching-Chih Tsai
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.106

Abstract

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). 
Attendance System with Face Recognition, Body Temperature, and Use of Mask using Multi-Task Cascaded Convolutional Neural Network (MTCNN) Method Noor Cholis Basjaruddin; Edi Rakhman; Yana Sudarsa; Moch Bilal Zaenal Asyikin; Septia Permana
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.109

Abstract

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.
Development of COVID-19 Isolation Facility Management System with Scrum Framework Sandy Darmowinoto; Syed Rafi Hossain; Puji Astuti
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.111

Abstract

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.
Big Data in Supply Chain Management: A Systematic Literature Review Johan Krisnanto Runtuk; Filson Sidjabat; Jsslynn; Felicia Jordan
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.115

Abstract

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. 
Effectiveness of Using Artificial Intelligence for Early Child Development Screening Michael-Lian Gau; Huong-Yong Ting; Teck-Hock Toh; Pui-Ying Wong; Pei-Jun Woo; Su-Woan Wo; Gek-Ling Tan
Green Intelligent Systems and Applications Vol. 3 Iss. 1 (2023)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.229

Abstract

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.
Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification Octarina Nur Samijayani; Tyan Permana Saputra; Hamzah Firdaus; Anwar Mujadin
Green Intelligent Systems and Applications Vol. 3 Iss. 1 (2023)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.244

Abstract

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.
Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate Teuku Alif Rafi Akbar; Catur Apriono
Green Intelligent Systems and Applications Vol. 3 Iss. 1 (2023)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.249

Abstract

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.
Real-Time Web-based Dashboard using Firebase for Automated Object Detection Applied on Conveyor Fadhillah Afira; Joni Welman Simatupang
Green Intelligent Systems and Applications Vol. 3 Iss. 1 (2023)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.251

Abstract

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.
Light Weight Native Edge Load Balancers for Edge Load Balancing P. Ravi Kumar; S. Rajagopalan; Joseph Charles P.
Green Intelligent Systems and Applications Vol. 3 Iss. 1 (2023)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.256

Abstract

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.

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