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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 45 Documents
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Search results for , issue "Vol 16, No 1: February 2026" : 45 Documents clear
Accessibility in e-government portals: a systematic mapping study Ouaziz, Mohammed Rida; Cheikhi, Laila; Idri, Ali; Abran, Alain
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp357-372

Abstract

In recent years, several researchers have investigated the challenges of accessibility in e-government portals and have contributed to many proposals for improvements. However, no comprehensive review has been conducted on this topic. This study aimed to survey and synthesize the published work on the accessibility of e-government portals for people with disabilities. We carried out a review using a systematic mapping study (SMS) to compile previous findings and provide comprehensive state-of-the-art. The SMS collected studies published between January 2000 and March 2025 were identified using an automated search in five known databases. In total, 112 primary studies were selected. The results showed a notable increase in interest and research activities related to accessibility in e-government portals. Journals are the most widely used publication channel; studies have mainly focused on evaluation research and show a commitment to inclusivity. “AChecker” and “Wave validator” are the most used accessibility evaluation tools. The findings also identified various accessibility guidelines, with the most frequently referenced being the web content accessibility guidelines (WCAG). Based on this study, several key implications emerge for researchers, and addressing them would be beneficial for researchers to advance e-government website accessibility in a meaningful way.
Optimal investment framework of static VAr compensators in distribution system based on life cycle cost Trung, Nguyen Hien; Thang, Vu Van
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp76-88

Abstract

The distribution system planning and operating present significant challenges due to low voltage, high impedance, and large load density, which lead to substantial power losses and low voltage quality. To address this challenge, the paper proposes an optimal framework for the simultaneous determination of the placement and sizing of static VAr compensators (SVCs) in DSs. The proposed model is formulated as an optimization problem that minimizes the life cycle cost, while accounting for the varying lifespans and investment times of SVCs. The framework incorporates hourly load variation and employs full alternating current (AC) power flow analysis to improve the accuracy of results. Additionally, it considers the dependency of the reactive power injected by SVCs on the DSs and incorporates the discrete rated capacities of SVCs to ensure practical feasibility and enhance the accuracy of compensation power, effect of DSs. The proposed approach is validated using a modified 33-bus IEEE test system implemented in the general algebraic modeling system (GAMS). Numerical results from multiple case studies confirm the feasibility and high performance of the proposed model.
Parameter-efficient fine-tuning of small language models for code generation: a comparative study of Gemma, Qwen 2.5 and Llama 3.2 Nguyen, Van-Viet; Nguyen, The-Vinh; Nguyen, Huu-Khanh; Vu, Duc-Quang
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp278-287

Abstract

Large language models (LLMs) have demonstrated impressive capabilities in code generation; however, their high computational demands, privacy limitations, and challenges in edge deployment restrict their practical use in domain-specific applications. This study explores the effectiveness of parameter efficient fine-tuning for small language models (SLMs) with fewer than 3 billion parameters. We adopt a hybrid approach that combines low-rank adaptation (LoRA) and 4-bit quantization (QLoRA) to reduce fine-tuning costs while preserving semantic consistency. Experiments on the CodeAlpaca-20k dataset reveal that SLMs fine-tuned with this method outperform larger baseline models, including Phi-3 Mini 4K base, in ROUGE-L. Notably, applying our approach to the LLaMA 3 3B and Qwen2.5 3B models yielded performance improvements of 54% and 55%, respectively, over untuned counterparts. We evaluate models developed by major artificial intelligence (AI) providers Google (Gemma 2B), Meta (LLaMA 3 1B/3B), and Alibaba (Qwen2.5 1.5B/3B) and show that parameter-efficient fine-tuning enables them to serve as cost-effective, high-performing alternatives to larger LLMs. These findings highlight the potential of SLMs as scalable solutions for domain-specific software engineering tasks, supporting broader adoption and democratization of neural code synthesis.
Hybrid neurocontrol of irrigation of field agricultural crops Kabildjanov, Aleksandr S.; Usmanov, Aziz M.; Yadgarova, Dilnoza B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp206-215

Abstract

This study investigates a conceptual framework for a hybrid intelligent control system designed to optimize the irrigation practice for field crops via fertigation technologies. This research is aimed at enhancing irrigation management through the improvement of the prediction, optimization, and regulation processes. This is achieved through the incorporation of modern computational intelligence with advanced deep learning based neural networks, evolutionary optimization algorithms, and the adaptive neuro-fuzzy technique. This hybrid control framework is made up of interconnected sets of monitoring and decision-making modules. These include subsystems for evaluation of soil conditions, monitoring of plant growth and physiological development, assessment of environmental and climatic conditions, and measurements of the intensity of solar radiation. Additional systems address the preparation of the fertigation mixture and control of intelligent decision-making processes. For this system, the overall control policy is rendered through a predictive neurocontrol approach with execution on a computer platform. A recurrent deep neural model, long short-term memory (LSTM) type, provides crop growth and development parameter predictions through the ability to explore temporal dependencies in agricultural processes. Optimization in the predictive control feedback is accomplished through genetic algorithms in an adaptive manner.
Tiny machine learning with convolutional neural network for intelligent radiation monitoring in nuclear installation Istofa, Istofa; Kusuma, Gina; Ningsih, Firliyani Rahmatia; Triyanto, Joko; Susila, I Putu; Susila, Atang
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp404-413

Abstract

This study focuses on developing an intelligent radiation monitoring system capable of operating on a low-power single-board computer (Raspberry Pi) for deployment in remote monitoring stations within nuclear facility environments. The proposed system utilizes a radionuclide identification method based on tiny machine learning (TinyML) with a convolutional neural network (CNN) architecture. The radionuclide dataset was acquired through measurements of standard radiation sources, with variations in distance, exposure time, and combinations of multiple sources-including Cs-137, Co-60, Cs-134, and Eu-152. The radiation intensity data from detector measurements were structured into a response matrix and subsequently converted into a grayscale image dataset for model training. Keras is used to design and train machine learning models, while Tensor Flow Lite is used to model size reduction. Experimental results demonstrate that the developed model achieves an accuracy of 99.338% for Keras model trained on computer and 84.568% after deployment on the Raspberry Pi. Furthermore, this study successfully designed and embedded the TinyML model into an environment radiation monitoring system at the PUSPIPTEK nuclear installation.
Stochastic planning of multi-bus hydrothermal systems using the scenario tree technique Camargo-Martínez, Martha Patricia; Ballesteros, Ricardo Rincón; Salazar-Caceres, Fabian; H., Andrés F. Panesso; Ramírez-Murillo, Harrynson; Añó, Osvaldo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp49-64

Abstract

Hydrothermal operation planning (HTOP) is a complex, large-scale optimal control problem. Traditionally, mathematical programming is used to solve it; however, metaheuristic techniques have emerged as an alternative approach. However, even in the context of current technological developments, the models developed to date generally require simplifications in the formulation. In particular, in medium-term planning, they have used a deterministic model or simplified transmission lines into a single bus. However, this approach leads to conservative and unrealistic solutions that may result in either oversizing or underutilization of resources. Therefore, this work proposes a methodology for incorporating uncertainties into the HTOP problem with a multi-bus topology. It was tested in a three-bus system, where linear functions are applied to simplify the production of hydroelectric plants and the cost of thermal units. The methodology incorporated well-established techniques in an implicit stochastic optimization (ISO) model, using a tree of 50 scenarios to model the hydrological series, which is solved with linear programming (LP). The results were validated with the costs of the 10000 generated series, showing an error of 5.07%. Additionally, the solutions were compared with an adapted metaheuristic technique for this problem to explore models applicable to more complex formulations.
Methods for identifying informative features in agricultural images Hudayberdiev, Mirzaakbar; Achilov, Baxodir; Alimkulov, Nurmukhammad; Koraboshev, Oybek; Abdirazakov, Fakhriddin; Sayfullaeva, Nargiza
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp256-277

Abstract

The paper deals with informative aspects of images, their scope and extraction methods. The research addresses numerous different types of features such as texture, color, geometric and structural features that play an important role in the field of image analysis and recognition. Contemporary extraction methods based on machine learning algorithms and fractal dimension are explained. The possibility of usage of these methods in real-life problems such as medical imaging, biometrics, remote sensing images processing and agriculture is considered. Successful implementation examples of information functions in real-life problems are presented and opportunities for further research on the topic are considered.
Enhancing Autonomous GIS with DeepSeek-Coder: an open-source large language model approach Nguyen, Kim-Son; Nguyen, The-Vinh; Nguyen, Van-Viet; Thi, Minh-Hue Luong; Nguyen, Huu-Khanh; Nguyen, Duc-Binh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp423-436

Abstract

Large language models (LLMs) have paved a way for geographic information system (GIS) that can solve spatial problems with minimal human intervention. However, current commercial LLM-based GIS solutions pose many limitations for researchers, such as proprietary APIs, high operational costs, and internet connectivity requirements, making them inaccessible in resource-constrained environments. To overcome this, this paper introduced the LLM-Geo framework with the DS-GeoAI platform, integrating the DeepSeek-Coder model (the open-source, lightweight version deepseek-coder-1.3b-base) running directly on Google Colab. This approach eliminates API dependence, thus reducing deployment costs, and ensures data independence and sovereignty. Despite having only 1.3 billion parameters, DeepSeek-Coder proved to be highly effective: generating accurate Python code for complex spatial analysis, achieving a success rate comparable to commercial solutions. After an automated debugging step, the system achieved 90% accuracy across three case studies. With its strong error- handling capabilities and intelligent sample data generation, DS-GeoAI proves highly adaptable to real-world challenges. Quantitative results showed a cost reduction of up to 99% compared to API-based solutions, while expanding access to advanced geo-AI technology for organizations with limited resources.
Systematic review of artificial intelligence applications in predicting solar photovoltaic power production efficiency Ikhsan, M. Rizki; Lakulu, Muhammad Modi; Pannesai, Ismail Yusuf; Rizali, Muhammad; Nugraha, Bayu; Swastina, Liliana
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp463-476

Abstract

The global energy crisis and climate change demand more accurate and efficient renewable energy forecasting methods. Solar photovoltaic (PV) systems offer abundant clean energy but their efficiency is highly affected by weather variability, requiring advanced predictive models. This systematic review of 69 studies published between 2020 and 2024 evaluates artificial intelligence (AI) and machine learning (ML) applications in PV forecasting, with a focus on hybrid algorithms such as convolutional neural network-long short-term memory (CNN-LSTM). Results demonstrate that hybrid models consistently outperform traditional statistical methods and standalone AI approaches by capturing spatiotemporal patterns more effectively, achieving significant error reductions and improving reliability. A notable gap identified is the limited integration of consumer behavior into forecasting models, despite evidence that incorporating demand-side patterns enhances accuracy. Challenges also remain in data availability, scalability across diverse climates, and computational requirements. This review contributes by synthesizing recent advances and emphasizing consumer integration as an underexplored but critical dimension for future research. The findings provide a foundation for developing more precise, resilient, and scalable PV forecasting models, supporting optimized energy management and accelerating the transition toward sustainable energy systems.
Machine learning-based prediction of moisture and oxygen in a large power transformer with online monitoring validation Ghazal, Osama T.; Assaf, Mohammed S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp1-9

Abstract

This study presents a predictive modeling approach for monitoring moisture and dissolved oxygen dynamics in a newly commissioned high-capacity power transformer. Using over 48,000 real-time observations collected across three years via an advanced online monitoring device installed on a 326 MVA generator step-up transformer (GSUT), machine learning models were developed to estimate moisture and oxygen concentrations based on correlated operational parameters. Multiple regression-based algorithms were trained and evaluated using performance metrics including root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Linear regression achieved superior performance with an RMSE values as low as 0.05888 ppm for oxygen and 0.0153 ppm for moisture. The models were further validated using data from a sister transformer, demonstrating generalizability and reliability across similar transformer units. This work contributes a scalable and accurate solution for real-time transformer health assessment, with practical implications for predictive maintenance strategies in power utilities.

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