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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 6,301 Documents
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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.
Neural-network based representation framework for adversary identification in internet of things Narasimhamurthy, Thanuja; Swamy, Gunavathi Hosahalli
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp6043-6052

Abstract

Machine learning is one of the potential solutions towards optimizing the security strength towards identifying complex forms of threats in internet of things (IoT). However, a review of existing machine learning-based approaches showcases their sub-optimal performance when exposed to dynamic forms of unseen threats without any a priori information during the training stage. Hence, this manuscript presents a novel machine-learning framework towards potential threat detection capable of identifying the underlying patterns of rapidly evolving threats. The proposed system uses a neural network-based learning model emphasizing representation learning where an explicit masked indexing mechanism is presented for high-level security against unknown and dynamic adversaries. The benchmarked outcome of the study shows to accomplish 11% maximized threat detection accuracy and 33% minimized algorithm processing time.
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.
Enhancing sexual education for children with special needs through augmented reality: development and evaluation of the Magical SeDu application Maria, Eny; Satria, Bagus; Andrea, Reza; Imron, Imron; Karim, Syafei; Ramadhani, Fajar; Suswanto, Suswanto; Putra, Emil Riza; Sjamsir, Hasbi
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.pp288-296

Abstract

This research focuses on the educational obstacles encountered by children with special needs (CWSN), specifically in sexual education, through developing and evaluating the Magical SeDu application. Using a three-phase instructional design model, the study followed the planning, design, and development phases to create user-centered features that meet diverse learning needs. User acceptance testing (UAT) further confirmed the usability and effectiveness of the app, with a satisfaction rating of 86.04%. These findings underscore the transformative potential of augmented reality (AR) technology in inclusive education, fostering interactive and visually stimulating learning experiences. The study also emphasizes the importance of involving stakeholders in the development process to ensure the app meets the specific needs of its users. Future research should focus on enhancing the app’s features and exploring its integration into broader educational environments to maintain accessibility and continuous improvement. This study contributes to the advancement of inclusive education strategies and highlights the critical role of sex education in increasing self-awareness and protection for children with special needs.
Systematic review of a business model using blockchain technology for the use of digital money in mass centers Medina, Julio César Rojas; Lengua, Miguel Ángel Cano
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.pp342-356

Abstract

In recent years, commercial transactions have experienced a radical change in the way goods and services are purchased. Payments with electronic and digital money are increasing dramatically compared to payments with physical money. Likewise, money using blockchain technology is marking disruptive milestones in transactions, especially in cross-border payments, showing many benefits, such as speed, lower costs, and security. The COVID-19 pandemic has shown the entire world the potential and possible development horizon of digital money, especially cryptoassets, in commercial transactions, as well as the risks associated with this technology. This has exposed the problem and need for a commercial model with blockchain technology for use in mass centers, which allows for the widespread and democratization of blockchain technology in mass commercial transactions. The methodology used is PRISMA. The objective of this article is to conduct a systematic review of the literature on digital money with blockchain technology for use in mass marketing centers. Finally, the results are presented, where the commercial model based on blockchain must consider security criteria, technology, legal aspects, and sociocultural barriers. Incorporate the interaction between electronic money, central bank digital currencies (CBDCs), and cryptoassets, as well as a decentralized technological platform for direct digital commerce. This implies that the model must consider these criteria in its design, implementation process, and the platform it supports.
Detection of islanding using empirical mode decomposition and support vector machine Patil, Balwant; Joshi, Diwakar; Santaji, Sagar; C. J., Sudhakar
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.pp10-24

Abstract

Accurate detection of islanding remains to be a challenge for grid connected microgrid system. An effective method to identify the islanding of microgrid has been presented which uses only the voltage at point of common coupling (PCC). Accurate islanding detection is necessary to impose appropriate control for the microgrid operation. Following the islanding of microgrid the intrinsic mode functions (IMF’s) of voltage at PCC obtained by empirical mode decomposition (EMD) will be analyzed by support vector machine (SVM) model which identifies the islanding of the microgrid. SVM model learns through the training data set. As many as 150 simulated cases have been used to train the SVM. A practical microgrid system has been simulated for various operating conditions and the data generation has been carried out by series of simulations for various islanding and non-islanding events using MATLAB Simulink. The proposed method gives optimistic results with high accuracy, zero non detection zone (NDZ) and detection time as low as 63.11 ms. Accurate islanding detection leads to smooth transition of microgrid control essential for operators.
Hybrid machine learning framework for chronic disease risk assessment Shadaksharappa, Harini; K. B., Rashmi; D. K., Shreyas; Mikali, Somanath; Gowda, Vishesh P.; C. A., Uday Shankar; Iyerr, Siddarth 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.pp321-332

Abstract

Chronic diseases like asthma, diabetes, stroke, and heart disease are the major causes of morbidity globally, which emphasizes the need for efficient predictive models to facilitate early detection and precautionary measures. Previous studies have used machine learning approaches for single-disease prediction, where models are designed for specific diseases, such as diabetes or heart disease. However, very few attempts have been made to develop unified frameworks for predicting multiple diseases simultaneously. This work presents a novel, unified framework using an ensemble of extreme gradient boosting classifier (XGBClassifier) and artificial neural networks (ANN) as individual classifiers to concurrently predict the risk of developing asthma, diabetes, stroke, and heart disease. This work follows a questionnaire-based approach that utilizes demographic, lifestyle, health metrics, symptoms and exposure-related data to create personalized risk assessments. The model achieves satisfactory accuracy rates of 95.82% for asthma, 96.68% for diabetes, 94.91% for stroke, and 94.52% for heart disease. The findings highlight how this novel hybrid model serves as an effective approach to tackle the intricate interactions between chronic ailments. The research also includes a user-friendly website that comprises a questionnaire and makes use of the best performing model to predict the probabilities of developing different diseases.

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