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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 47 Documents
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Harnessing Smart Farming: Key Determinants of Automated Mini Greenhouse Adoption and Use in the Philippines Zhuo, Eugenia R.
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.527

Abstract

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.
Fraud Classification in Online Payments Using Supervised Machine Learning Algorithms Editya, Arda Surya; Alamin, Moch. Machlul; Pramana, Anggay Lury; Kurniati, Neny
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.552

Abstract

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.
Classification of Metal Surface Defects Using Convolutional Neural Networks (CNN) Pratama, Dhika Wahyu; Ismail, Muchammad; Nurraudah, Restu; Rifai, Achmad Pratama; Nguyen , Huu Tho
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.581

Abstract

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.
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
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.606

Abstract

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.
A Systematic Literature Review of YOLO and IoT Applications in Smart Waste Management Gelar, Trisna; Fitriani, Sofy; Rachmat, Setiadi
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.706

Abstract

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.
Design and Build a Push-Pull Inverter for Room Lighting Haryanti, Munnik; Yulianti, Bekti; Rahmawati, Cynthia; Adhicandra, Iwan
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.761

Abstract

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.
Implementation of Key Performance Indicators in the Palm Oil Harvest Monitoring Information System Rina Sari, Diah Ayu; Irawan, Muhammad Dedi
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.782

Abstract

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.

Page 5 of 5 | Total Record : 47

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