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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 68 Documents
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Integration of Naïve Bayes-Based Stunting Status Classification and GIS Hotspot Mapping for the Identification of Priority Areas in Tomohon City, Indonesia Eunice Emely Eurika Pitoy; Chatreen Rindu Ceyzia Pontoh; Marike Kondoj; Herry Langi; Maksy Sendiang
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1190

Abstract

Stunting remained a public health problem that required data- and area-based monitoring so that interventions could be implemented in a targeted manner. This study aimed to develop an integrated system for classifying stunting status and identifying priority areas in Tomohon City through the combination of WHO Z-Score standards, the Naïve Bayes algorithm, prevalence calculation, and hotspot mapping based on a Geographic Information System (GIS). This study employed a Research and Development (R&D) approach consisting of needs analysis, design, implementation, testing, and evaluation stages. Toddler data were obtained from the Tomohon City Health Office, including age, sex, height or body length, weight, residential area, urban village, district, and community health center. The system was developed using MySQL, Python, PHP Framework CodeIgniter 3, and GIS. The results showed that the system was able to classify toddlers’ nutritional status into normal, stunted, and severely stunted categories, calculate prevalence by urban village, and display the distribution of cases in the form of a digital map. Gaussian Naïve Bayes modeling using 970 training data points and 243 testing data points produced an accuracy of 94.7%, precision of 31.6%, recall of 33.3%, and F1-score of 32.4%. GIS hotspot visualization helped identify priority areas, although data coverage still needed to be expanded to make the results more representative.
Preliminary Study of ResNet-Based Facial Identification for Access Control Systems Andre Kurniawan; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1197

Abstract

Facial recognition is an important application of artificial intelligence (AI) and computer vision in modern security systems, particularly for automated access control. This study aimed to implement and evaluate a Residual Network (ResNet)-based facial identification system for door access control applications. A publicly available facial image dataset was used and was divided into training (70%), validation (20%), and testing (10%) subsets. The proposed methodology consisted of data preprocessing, ResNet-based model training, and performance evaluation using accuracy and loss metrics. The model was trained for 10 epochs to assess its initial learning capability. The experimental results showed relatively low performance, with training accuracy ranging from 3.6% to 3.8% and validation accuracy of approximately 3.6%, while loss values remained high throughout the training process. These findings indicated that the model was unable to effectively learn discriminative facial features from the dataset and exhibited signs of underfitting. The limited performance was likely associated with insufficient dataset diversity, suboptimal preprocessing procedures, and non-optimized training parameters. The study highlighted the challenges of implementing ResNet-based facial recognition systems under constrained training conditions. Future work should focus on expanding the dataset, applying data augmentation techniques, optimizing hyperparameters, and utilizing pretrained models to improve recognition performance and system reliability.
Implementation of a Centralized Cloud-Based Hotspot Voucher Management System and Network Traffic Monitoring Using MikroTik Virtual Cristovani Ari Wibowo Lohonauman; Christopel Hamonangan Simanjuntak; Maksy Sendiang; Herry Setiawan Langi; Venny Vita Ponggawa
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1215

Abstract

Hotspot voucher services became one of the most widely adopted solutions among local Internet Service Providers (ISPs) because they facilitated user access control, usage duration management, and bandwidth allocation. In the existing network environment, hotspot voucher sales were operated at two different locations. However, the management of these locations was still performed independently using separate MikroTik routers. This condition created several challenges, including decentralized voucher data, standalone user authentication processes, inefficient voucher administration, and the inability to perform comprehensive network traffic monitoring. This study implemented a centralized hotspot voucher management system using a MikroTik Cloud Hosted Router (CHR) to integrate two hotspot voucher sales locations into a unified management platform. The MikroTik CHR was deployed in a cloud computing environment and functioned as the central server for voucher management, user authentication, and network traffic monitoring. Each MikroTik router at the voucher sales locations was connected to the MikroTik CHR through a VPN tunnel. Furthermore, a web-based application was developed as a management interface and was integrated with the MikroTik CHR through an Application Programming Interface (API). The application enabled administrators to generate vouchers, monitor voucher status, manage users, and observe network traffic through a centralized dashboard. The research adopted the Network Development Life Cycle (NDLC) methodology, which consisted of the stages of analysis, design, simulation, implementation, monitoring, and management. The implementation results demonstrated that the centralized system successfully integrated voucher management across two different locations, simplified administrative processes, supported centralized user authentication, and provided unified network traffic monitoring through a single platform. Therefore, the implementation of MikroTik CHR in a centralized hotspot voucher system improved management efficiency and supported the expansion and sustainability of hotspot services across multiple locations.
Classification of Leaf Diseases in Guava Plants Based on Images Using the MobileNetV3 Model Rikky; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1239

Abstract

Guava cultivation is often threatened by leaf diseases that disrupted plant growth and reduced agricultural productivity. Early and accurate disease identification was crucial but relied heavily on slow and subjective visual inspections by human experts. This study proposed an automated, accurate, and efficient solution by comparing lightweight deep learning models. A total of 656 augmented guava leaf images representing four classes (Algal Leaf Spot, Insects Eaten, Red Rust, and Healthy Leaf) were evaluated. Using a transfer learning approach, the hyperparameters were systematically tuned for MobileNetV3-Small as the proposed model and compared with MobileNetV2 as the baseline architecture. The experimental results demonstrated that MobileNetV3-Small achieved a superior test accuracy of 91.00%, outperforming MobileNetV2, which achieved 87.00%. The integration of Squeeze-and-Excitation (SE) modules and the h-swish activation function in MobileNetV3-Small significantly improved the identification of subtle visual symptoms, particularly for the Healthy Leaf and Insects Eaten classes. However, MobileNetV2 maintained a slight advantage in real-time processing speed (54.86 FPS versus 50.30 FPS) because of memory-bound bottlenecks associated with the SE modules. Overall, MobileNetV3-Small provided superior diagnostic accuracy, whereas MobileNetV2 remained a highly viable option for latency-critical deployment on low-end devices.
Preprocessing and Adaptive Parsing of Unstructured Voter Turnout Data with Spatial Feature Enrichment Arya Pratama Tarigan; Mohammad Andri Budiman; Ade Candra
Green Intelligent Systems and Applications Volume 6 - Issue 2 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i2.1282

Abstract

The raw input data exhibited severe structural heterogeneity, manifesting as seven distinct structural formats (Formats A–G) characterized by inconsistent header placement, nested merged cells, variable string representations, and missing values. To address these challenges without relying on static rule-based scripts, an intelligent adaptive parser was developed using dynamic regular expressions (regex) and token-density schema mapping based on normalized Levenshtein distance. The adaptive pipeline automatically identified table structures and standardized heterogeneous column definitions into a unified relational database, achieving a data-cleaning accuracy of 99.4%. To enhance the analytical value of conventional demographic data, a spatial feature enrichment pipeline was implemented by integrating three external Application Programming Interfaces (APIs): OpenStreetMap Nominatim for high-precision geocoding, the Google Maps Distance Matrix API for calculating actual road-network distances and travel times, and the Open-Meteo Elevation API for extracting elevation data. This study contributed to public sector data engineering by demonstrating that the integration of automated adaptive parsing with geospatial multi-API enrichment successfully transformed fragmented public documents into a high-fidelity, multidimensional data repository suitable for robust spatial–demographic policy analysis.
Performance Analysis of the Multivariate Multiple Linear Regression Algorithm for Economic Growth Based on Oil Palm Plantation Land Expansion in North Sumatra Province Afridayani; Erna Budhiarti Nababan; Baihaqi Siregar
Green Intelligent Systems and Applications Volume 6 - Issue 2 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i2.1294

Abstract

Oil palm plantations are one of the main sectors contributing to regional economic development in North Sumatra Province. This study aimed to analyze the performance of the Multivariate Multiple Linear Regression (MMLR) algorithm in modeling the relationship between oil palm plantation expansion and regional economic indicators. Secondary data from five oil palm-producing regencies covering the period 2013–2023 were obtained from the Central Statistics Agency (BPS). The independent variables consisted of plantation area and oil palm production, whereas the dependent variables were Gross Regional Domestic Product (GRDP) and per capita income. The dataset was divided into training (2013–2019) and testing (2020–2023) subsets. Model performance was evaluated using the coefficient of determination (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results showed that the MMLR model was able to capture the relationship between oil palm plantation expansion and regional economic indicators, although its predictive performance varied across regencies. These findings indicated that the model provided useful insights into the contribution of the oil palm plantation sector to regional economic development in North Sumatra.
Validation of a Portable Resistive-Sensor Corn Moisture Meter against a Standard Grain Meter Subhan Fahmi Nasution; Sally Irvina Ritonga; Masherlina; Amty Ma'rufah Ardhiyah Dalimunthe; Abdul Floranda
Green Intelligent Systems and Applications Volume 6 - Issue 2 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i2.1286

Abstract

Measuring the moisture content of corn kernels was a critical aspect of post-harvest handling, as moisture content directly affected product quality and shelf life. This study validated a portable resistive-sensor-based instrument for measuring corn kernel moisture content by comparing its readings with those obtained using a standard grain moisture meter. The validation involved ten corn kernel samples with moisture contents ranging from 12% to 14% on a wet basis. Each sample was measured using both the standard moisture meter and the portable resistive-sensor instrument under identical conditions. Measurements obtained from the two devices were compared to evaluate their agreement using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed that the portable resistive-sensor instrument achieved an MAE of 0.42% and an RMSE of 0.44% relative to the standard device. These low error values demonstrated that the developed instrument produced consistent measurements and accurately tracked variations in corn kernel moisture content. In addition to its measurement accuracy, the instrument offered several practical advantages, including a simple design, relatively low implementation cost, portability, rapid measurement, and suitability for on-site corn quality assessment. Overall, the resistive-sensor-based instrument provided an economical, practical, and readily implementable alternative for moisture measurement, with potential applications in supporting the drying and storage of agricultural commodities.
Analysis of the Effect of Attributes on Single Tuition Fee Grouping Using Multiple Linear Regression and Extreme Gradient Boosting Dea Annona Prayetno Putri; Erna Budhiarti Nababan; Mohammad Andri Budiman
Green Intelligent Systems and Applications Volume 6 - Issue 2 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i2.1289

Abstract

Objectively determining the Single Tuition Fee (UKT) level was essential for aligning educational costs with students’ socioeconomic conditions. This study compared Multiple Linear Regression (MLR) and Extreme Gradient Boosting (XGBoost) for predicting the final (Pleno) UKT level using data from 12,355 students, including socioeconomic, school-related, and administrative attributes. Data preprocessing involved cleaning, handling missing values and duplicates, transforming categorical variables, and splitting the dataset into training and testing sets (80:20). Models were evaluated under “operational” and “strict” scenarios using accuracy, precision, recall, F1-score, confusion matrices, and AUC. In the operational scenario, XGBoost achieved 93.42% training and 90.15% testing accuracy, outperforming MLR, which achieved 84.10% testing accuracy. Under the strict scenario, XGBoost accuracy decreased to 63.27–63.54%, compared with 49.74–49.86% for MLR. These findings demonstrated XGBoost’s greater ability to capture nonlinear relationships and complex attribute interactions. Permutation importance identified Coordinator- and Verifier-assigned UKT as the dominant predictors, while SHAP analysis identified per capita income as the most influential socioeconomic factor. Overall, XGBoost showed superior predictive performance but should be used as a decision-support tool rather than as the sole determinant of student tuition fees.

Page 7 of 7 | Total Record : 68

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