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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
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.
Advances in AI, IoT, and smart systems for emerging electrical and computer engineering applications Sutikno, Tole
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.pp555-558

Abstract

The current issue of the International Journal of Electrical and Computer Engineering (IJECE) showcases a diverse array of research at the intersection of artificial intelligence (AI), Internet of Things (IoT), machine learning (ML), and advanced engineering systems. Highlighted studies explore the application of autonomous mobile robots for logistics and material handling, sensorless control and acceleration profiling of electric drives, hybrid control strategies for high-performance electric vehicles, and deep learning methods for image recognition, emotion detection, and software fault prediction. Further contributions address practical implementations of IoT in heatstroke prevention, hydroponics, Spirulina cultivation, and energy-efficient greenhouse management, demonstrating how intelligent systems can optimize resource use, safety, and productivity. The issue also emphasizes AI-empowered modeling in accelerator design, solar photovoltaic power forecasting, and GIS automation, while exploring cybersecurity through intrusion detection frameworks and fraud detection in financial systems. Cutting-edge deep learning models such as convolutional neural networks (CNN), vision transformers, and TinyML are leveraged for healthcare, nuclear monitoring, and prenatal diagnostics. Collectively, these contributions underline the transformative role of AI, IoT, and hybrid intelligent systems in electrical and computer engineering, bridging theoretical advances with practical, real-world applications. This issue aims to inspire continued research and development toward efficient, secure, and adaptive technologies that advance smart engineering solutions worldwide.

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