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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 6,345 Documents
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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.
Hyperparameter tuning of MobileNetV2 on forest and land fire severity classification Hidayat, Assad; Sitanggang, Imas Sukaesih; Syaufina, Lailan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp964-972

Abstract

Forest and land fires pose significant environmental challenges, causing economic and ecological damage depending on their severity. This study proposes a deep learning-based classification model to assess fire severity using the MobileNetV2 architecture. A dataset of 560 post-fire images was categorized into five severity levels, with dataset preprocessing involving resizing, rescaling, and image augmentation. To enhance model performance, K-means clustering was applied for balanced data distribution across classes. The model was trained using grid search for hyperparameter tuning, with the optimal combination being a batch size of 8, learning rate of 0.0001, and dropout of 0.3. Training was conducted in 50 epochs, and evaluation using the confusion matrix demonstrated an accuracy of 85%, precision of 86%, and recall of 81%. The results indicate that MobileNetV2 effectively classifies post-fire severity levels, offering a reliable tool for post-disaster assessment. This study highlights the significance of dataset preprocessing and hyperparameter tuning in improving model accuracy. Future research should explore alternative architectures and expand the dataset to enhance model generalization. These findings can aid authorities in assessing fire impact, supporting mitigation strategies, and improving post-fire land management.
Photovoltaic storage system enhancement-based supercapacitor control Soliman, Ahmed Mahmoud; Elbaset, Adel A.; Eldeen, Ashraf Nasr
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp629-637

Abstract

This paper discusses the improvement of the storage system by getting a stable voltage, with a large inrush current for the battery. The battery system (BESS) is the most important component of a photovoltaic (PV) system. Its large size allows it to provide the desired high peak discharge currents and extend its lifespan. Our work focuses on control the integration of super capacitors (SC) with batteries in order to maximize the battery's power supply, reduce the ripples caused by light changes photovoltaic cells, improve the battery lifespan and supply the useful high peak power for a short periods of time for the big loads (like motors, trains, and big mechanisms,), Super capacitors (SCs) can do that since their internal architecture does not include chemical solutions, which will result in high power densities and higher charge and discharge currents, also lower energy densities. These lower energy densities will be compensated by a combination and integration with the battery, especially the lead-acid battery. Focusing on the lead acid due to drawbacks like short lifetime, low number of cycles. from that combination by switching the control circuit, it can increase the battery lifetime and remove the stress, especially in high current loads, reducing abnormal battery temperature, and ensuring a significant mass reduction of the energy storage system as all. Also, by supporting the SC with a buck boost converter control, keeping the voltage stable, preventing the PV voltage changing problems from the PV cell to any storage systems.
Internet of things-based smart control and comfort classification system for broiler chicken coops using k-nearest neighbor algorithm Amiroh, Khodijah; Widyantara, Helmy; Hariyanto, Muhammad Dwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1039-1050

Abstract

The poultry industry increasingly relies on environmental automation to improve broiler chicken welfare and productivity. Prior studies have implemented threshold-based systems to control coop conditions, typically activating actuators based on fixed values of temperature or humidity. However, such systems lack adaptability to dynamic environmental interactions and often result in inefficient energy use and overactivation. This study proposes a novel low-cost internet of things (IoT)-based smart poultry coop system that combines real-time environmental sensing with comfort classification using the k-nearest neighbor (KNN) algorithm. The system monitors temperature, humidity, and ammonia levels through affordable sensors integrated with an ESP32 microcontroller, then transmits data via message queuing telemetry transport (MQTT) to a remote server for classification and control decision-making. Control logic is applied to activate fans, heating lamps, or humidifiers accordingly. Evaluation on a mini coop prototype demonstrated a classification accuracy of 92.2% and a 34% reduction in actuator overactivation compared to threshold-based systems. Environmental stability improved by 23%, and energy usage decreased by 12.6%. The system also features user interfaces via Telegram and Blynk, proven intuitive through informal testing. These results validate the feasibility of integrating machine learning into small-scale poultry environments, offering an intelligent, scalable, and user-friendly solution that outperforms traditional methods.
A smart real-time system for autonomous detection and prevention of LPG gas leaks with theoretical validation Hosur, Ravi; Noola, Daneshwari A; Hiremath, Ananda S; Rati, Arpita; Dalawai, Pavitra; Gornal, Ravichandra; Patil, Reena; Patil, Shashidhar B; Alur, Sunanda
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp766-790

Abstract

A gas leak sensor usually only sends a warning signal, leading to a delay in treatment and increased danger. The proposed study aims to develop an autonomous liquefied petroleum gas (LPG) gas leak detection system along with containment, using an MQ-5 sensor, Arduino Uno, and a stepper motor-based shut-off valve. The proposed system can detect a gas leak in 2.3 seconds and contain it in 3.1 seconds, with a high accuracy rate of 97.5%. The proposed system also offers a major advantage over other gas leak detection systems that only rely on global system for mobile communication (GSM) or internet of things (IoT) based warning messages by using adaptive control with a proportional-integral-derivative (PID) controller, a Laplace transform model, and exponential gas diffusion analysis. The proposed system also includes a feature to sense a smoke alarm and power failure, along with abnormality detection using artificial intelligence (AI)-based analysis. The proposed system is also efficient in terms of power consumption, with an average power consumption of only 2.1 W. The proposed system thus enhances a shift from passive detection to active containment in a smart system, thereby contributing to the domain of electrical and control engineering.
The ethics of AI technology in academic work: assessing the line between assistance and plagiarism Patwary, Md. Owafeeuzzaman; Islam, Md. Reazul; Islam, Abtahi; Khan, Nur-e Sarjina; Al–Jubair, Md. Abdullah; Hossen, Md. Jakir; Mridha, M. F.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp924-944

Abstract

The integration of artificial intelligence (AI) into academia has transformed educational practices and enhanced personalized learning and problem-solving capabilities. However, this raises significant ethical concerns regarding the balance between legitimate assistance and plagiarism. This study investigated public perceptions of AI in academic settings, focusing on its impact on effectiveness, dependency, and ethical considerations of AI use. A survey of 498 respondents from various educational roles was conducted, and the data were analyzed using SPSS for descriptive statistics, chi-square tests, and regression analyses. The results identified a significant correlation between people’s educational roles and their interaction with AI tools (χ2(6) = 16.488, p = 0.036), reflecting the diverse patterns of interaction within the academic community. More frequent use of AI was linked to less dependency (β = −0.298, p < 0.001), contradicting the widespread belief of over-reliance on AI. Age and educational role had limited explanatory value in perception of AI dependency issues (R2 = 0.033). The findings indicate a strong correlation between AI usage frequency and dependency levels, with increased exposure to AI fostering a more critical approach rather than a dependent one. Concerns regarding the unethical use of AI, inaccuracies in AI-generated content, and the need for clear institutional policies were also highlighted. This study underscores the importance of responsible AI integration, advocating for ethical frameworks and educational interventions to ensure that AI enhances learning without compromising academic integrity.
Smartphone data privacy and security awareness among university students in Malaysia Al-Rassas, Ahmed; Abidin, Zaheera Zainal
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp850-862

Abstract

This study examines the level of data privacy and security awareness (DPSA) among Malaysian university students who depend on smartphones for academic activities. An enhanced cybersecurity education (CE) technological proficiency–perceived control (CTP) model is proposed, incorporating technological innovation and cultural norms (TICN) as a mediating factor between technological proficiency (TP) and awareness. A total of 356 students from public and private institutions in Melaka participated. The Krejcie and Morgan table was used to determine the sample size. Descriptive analysis was conducted using IBM SPSS 27, and SmartPLS-SEM was used to evaluate both measurement and structural models. Reliability and validity were confirmed through a pilot study with 50 respondents. Findings show that TICN significantly strengthens the translation of technical skills into protective behavior, outperforming the original model that used frequency of smartphone usage (FSU) as a mediator. The enhanced model provides a deeper understanding of the socio-technical determinants of smartphone privacy awareness. Implications, limitations, and directions for future research are also discussed.
Wearable and implantable antennas for healthcare applications: advancements, challenges, and future directions P., Sameera; Desai, Priyadarshini K.; Kulkarni, Keerthi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp827-841

Abstract

The rise of personalized and remote healthcare solutions has accelerated the demand for reliable wireless communication systems integrated into medical devices. Among these, wearable and implantable antennas play a crucial role by enabling the seamless exchange of data between in-body or on-body sensors and external monitoring equipment. These antennas are key components in systems designed for continuous health monitoring, early diagnosis, and patient rehabilitation. Unlike conventional antennas, those used in medical applications must function efficiently in close contact with or inside the human body, often under challenging conditions such as body movement, varying tissue properties, and limited space. As a result, the design and development of these antennas require careful consideration of factors like flexibility, biocompatibility, low power operation, and electromagnetic safety. This study reviews recent publications from 2017 onwards on wearable and implantable antennas. The material type, operating frequency band, and operational environment are considered for the design of the wearable and implantable antenna. To minimize loss, the research employed a high-thickness substrate, gold, and graphene material for the radiating patch in most of the design. This review presents a detailed overview of recent advancements in wearable and implantable antennas tailored for healthcare applications, highlights current design challenges, and outlines future research opportunities in this rapidly evolving field.
Development of BAPOLAIC: AI chatbot for optical character recognition based-document extraction and voice assistant Fahreji, Rival; Wijaya, Ryan Satria
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1002-1009

Abstract

Conventional chatbots often lack integrated functionalities for complex academic tasks, such as multi-format document handling and multimodal interaction. This paper presents the design, implementation, and performance evaluation of BAPOLAIC, a web-based, multimodal AI assistant developed to address this gap. The system architecture integrates optical character recognition (OCR), a dual-strategy natural language processing (NLP) module, and voice assistance, all orchestrated by the Gemini API. Quantitative evaluation confirmed high performance: the OCR module achieved a 98.69% average accuracy, and the retrieval-based NLP path correctly handled 90% of test queries. Furthermore, the API integration demonstrated exceptional efficiency with a median latency as low as 0.06 ms. Task-based evaluations validated BAPOLAIC's effectiveness in performing intelligent functions like summarization and content-based Q&A, with a superior capacity for handling up to 10 consecutive documents. The results validate BAPOLAIC as a successful proof-of-concept for a specialized academic tool, providing a framework for integrating multiple AI technologies to enhance educational productivity.
Design and simulation of an electric vehicle charger with integrated interleaved boost converter and phase-shifted full-bridge converter using MATLAB/Simulink Samosir, Ahmad Saudi; Sutikno, Tole; Huda, Alfin Fitrohul; Mardiyah, Luthfiyyatun
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp687-698

Abstract

This paper presents the design and simulation of a high-efficiency electric vehicle (EV) charger that integrates a two-phase interleaved boost converter (IBC) with a phase-shifted full-bridge (PSFB) converter using MATLAB/Simulink. In contrast to existing studies that treat these converter stages independently, this work introduces a unified AC–DC–DC architecture that simultaneously minimizes input current ripple, improves DC-bus stability, and enables soft-switching operation for reduced switching losses. The values of the inductors and capacitors are derived analytically based on ripple constraints and switching frequency considerations, and example calculations are explicitly provided. Simulation results demonstrate that the proposed charger maintains a stable 600-V DC bus with less than 2% voltage ripple, delivers a controlled charging current of 100 A with 3 A ripple, and achieves an overall efficiency of 95%. These findings indicate that the integrated interleaved–PSFB topology provides superior conversion efficiency and power quality, making it a strong candidate for future EV fast-charging infrastructure.

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