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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 6,439 Documents
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A hybrid retrieval augmented generation framework for automated educational document understanding and intelligent response generation Basavesh D.; Jayashree Nagaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1964-1975

Abstract

New students often struggle when short articles clash with thick textbooks. Still, even though large language models offer some teaching support, standard online setups lack focused accuracy - sometimes making things up - and risk user data control. Here comes an idea: build a tightly tested, self- contained system that aligns learning materials automatically without needing the internet, keeping information private by design. One look at two setups shows how they handle local reasoning differently. Instead of using both encoder and decoder parts, one system skips the encoder entirely. That simpler design grabs full context through ChromaDB without shrinking the data first. Meanwhile, the older type crunches input down, losing meaning along the way. Even though it runs fast - just under a second - errors pop up often, four out of five responses drifting off course. On the flip side, the new method builds correct code nearly every time, adds clear explanations tied to lesson goals, yet takes more than fourteen seconds to reply. Slower? Yes. More accurate? Clearly. What stands out is how compressed models running locally can still catch up in understanding classroom content. Another key point emerges: building tutors powered by artificial intelligence (AI) becomes safer when data never leaves the device and outside services are not needed at all.
Beyond adoption: measuring the success of mandatory information systems through an integrated ECM and ISSM Muhammad Rosyid Ridlo; Muhammad Fachri Shandika Iman; Reny Yuliati
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2031-2041

Abstract

The successful implementation of mandatory organizational information systems depends not only on system adoption but also on user satisfaction. However, most post-adoption evaluation studies have focused on voluntary systems, leaving mandatory public sector deployments substantially underexplored. This study evaluates the determinants of employee satisfaction with the Coretax Administration System, a nationwide integrated tax platform implemented by the Directorate General of Taxes in Indonesia. To provide a comprehensive explanation of post-adoption evaluation, this research integrates the Expectation Confirmation Model (ECM) and the Information System Success Model (ISSM), examining how system quality, information quality, and service quality influence confirmation and perceived usefulness, which in turn determine user satisfaction. Using a quantitative approach, data were collected from 292 employees actively using the system and analyzed through Partial Least Square Structural Equation Modeling (PLS-SEM). The results demonstrate that the integrated model exhibits strong predictive power, explaining 76.4% of the variance in user satisfaction. System quality emerged as the most influential determinant, significantly affecting confirmation and perceived usefulness, which subsequently drives satisfaction. Meanwhile, information quality and service quality showed selective effects, indicating that technical reliability plays a more critical role than supportive features in a mandatory environment. The findings offer actionable guidance for policymakers and IS architects engaged in large-scale compulsory digital transformation initiatives in the public sector.
Intelligent routing-based attack detection in Internet of Things networks using artificial intelligence Huda Saloom Sultan; Asseel Jabbar Almahdi; Murteza Hanoon Tuama; Athar Hussein Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2169-2181

Abstract

The fast-growing Internet of Things (IoT) networks have posed considerable security risks because of decentralized network designs, dynamic topologies, and inadequate computation capabilities. Current intrusion detection strategies are primarily traffic-based, but without paying attention to routing-layer dynamics, which are paramount in multi-hop IoT systems. To overcome this drawback, this paper suggests a smart routing-conscious attack detection model which combines routing-layer monitoring with methods of artificial intelligence to improve the security of IoT networks. The suggested framework constantly compares routing metrics, such as packet loss, change in hop count, end to end delay and energy consumption to detect malicious routing behavior in real time. Two types of artificial neural networks, feedforward neural network (FFNN) and convolutional neural network (CNN) are used to categorize routing activities as normal or malicious. The experimentation on simulation was carried out by using NS-2 in a dynamic multi-hop IoT environment where routing-based DoS attacks were implemented. The experimental results reveal that CNN model had a higher detection accuracy of 85.76% with lower execution time of 17 s compared to the FFNN model which had an accuracy of 82.76% and an execution time of 18 s. Moreover, the suggested framework enhanced reliability of routing by minimizing the packet loss and communication delay and having low routing overhead. These results support the hypothesis that routing-aware intelligence can be used to enhance AI-based intrusion detection to create an adaptive, routing-aware, and resource-efficient security solution to decentralized networks of IoT devices.
Open data for clinical AI: a comprehensive review of disease prediction datasets Sindhu Rajendran; Chandrashekar B. S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1955-1963

Abstract

With the advancements in the medical sector world-wide, the use of machine learning has been in use. In order to use these machine learning models for prediction and diagnosis of certain diseases one of the main components is datasets. The need for high-quality datasets in healthcare prediction models is critical due to the data-driven nature of machine learning. These models rely on comprehensive, accurate, and representative datasets to make reliable predictions that can impact real-world patient outcomes. This paper provides an insight about the different components in the datasets present for diseases such as osteoporosis, heart disease, diabetes, respiratory, syncytial virus, interactive thyroid, Parkinson’s and sepsis. Also, a comparative study on the parameters of the datasets in the Indian perspective and globally are also discussed.
A convolutional neural network -based driver monitoring system for drowsiness and distraction detection Sara Benkouider; Nasreddine Lagraa
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2220-2229

Abstract

Road accidents caused by driver drowsiness and distraction are a major global concern, as fatigue and inattention significantly slow reaction time and increase accident risk. To address this issue, this paper proposes a vision-based driver monitoring system using facial analysis from an on-board camera. The system detects the face and extracts key regions of interest, including the eyes, mouth, and head, which are analyzed independently using convolutional neural networks. Temporal information is captured by aggregating the convolutional neural network (CNN) outputs over a fixed time window. Drowsiness is estimated by fusing eye and mouth features with a multilayer perceptron, while distraction is detected based on head movements. An important advantage of the proposed approach is its robustness to partial input loss, allowing the system to remain functional even when some facial regions are missing or occluded, such as when wearing sunglasses or face masks. Experimental results show high detection accuracy, reaching 97.3% for drowsiness and 98% for distraction under ideal conditions, with only limited performance degradation in challenging scenarios. These results confirm the suitability of the proposed system for real-time driver monitoring applications.
Physics-based modeling of cobalt-doped nickel-zinc on-chip ferrite inductors Bambang Mulyo Raharjo; Dicky Rezky Munazat; Sudirman Rohadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1805-1816

Abstract

The miniaturization of integrated voltage regulators (IVRs) for multi-core processors is fundamentally bottlenecked by the high-frequency magnetic losses of conventional inductor cores. This study presents a rigorous computational framework to optimize Cobalt-doped Nickel-Zinc ferrite (Ni_(1-x) Zn_0.4 Co_x Fe_2 O_4) for 10 MHz on-chip power delivery. Utilizing Landau-Lifshitz-Gilbert (LLG) relaxation dynamics and Maxwell-Wagner interfacial polarization, the complex electromagnetic dispersion was modeled and quantitatively validated against recent empirical literature. Furthermore, high-temperature power loss density was bounded using the trust region reflective (TRF) numerical curve fitting algorithm to validate an anisotropy-compensated "thermal valley" at 80 °C. A multi-objective sensitivity analysis identified a high-efficiency Cobalt "sweet spot" at a concentration of x=0.04. This specific formulation optimally stiffens domain walls, safely shifting the resonance frequency to 40 MHz and maximizing the quality factor (Q) at the 10 MHz operational target. When applied to a simulated 3.5 V to 1.0 V DC-DC buck converter, the optimized x=0.04 core demonstrated its adequacy for on-chip applications by maintaining >90% power efficiency under a rigorous 2.0 A load. These predictive results mathematically prove that precision Cobalt doping is a highly viable strategy for suppressing parasitic losses in next-generation 3D-IC power delivery networks.
From climate time series to planting windows in chili (Capsicum frutescens): a SARIMA–SVM–XGBoost framework with balanced-accuracy thresholding Efrans Christian; Nova Noor Kamala Sari; Ressa Priskila; Septian Geges
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2210-2219

Abstract

This study proposes a spatio-temporal decision-support framework that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to derive adaptive planting windows for chili (Capsicum frutescens) at the sub-district level. The framework addresses key challenges in climate-sensitive agriculture, including spatial data leakage and class imbalance, by employing Leave-One-Group-Out (LOGO) cross-validation and Balanced Accuracy–based threshold optimization. The proposed system transforms heterogeneous environmental data into actionable recommendations by combining climate forecasting, land suitability assessment, and yield prediction within a unified pipeline. Experimental results indicate that the framework effectively captures seasonal climate dynamics and produces consistent planting recommendations aligned with agronomic conditions, enabling multiple planting cycles per year. The primary contribution of this work lies in a transparent and generalizable integration of statistical and machine learning models into a practical decision-support framework. The proposed approach bridges predictive modeling and real-world agricultural decision-making and can be extended to other crops and regions for climate-adaptive agricultural planning.
Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm Chutipongse Boonyakitmaitree; Suchada Sitjongsataporn
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1817-1831

Abstract

As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
SS-ANFIS: a semi-supervised neuro-fuzzy model for offline signature verification Sadly Syamsuddin; Jufri Jufri; Suci Rahma Dani Rachman; Suryani Suryani; Wilem Musu; Salmiati Salmiati; Yesycha Arun Mangopo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1985-1997

Abstract

Signature verification remains a critical authentication mechanism in academic and administrative environments, yet manual verification is vulnerable to forgery and subjective judgment. This study proposes SS- ANFIS, a semi-supervised neuro-fuzzy model for offline signature verification under limited labeled data conditions. The proposed model integrates pseudo-label-based self-training into a Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system (ANFIS). Static image-based features were extracted from offline signature images and transformed using principal component analysis (PCA) before classification. Experiments were conducted on 800 offline signature samples collected from Dipa University Makassar, consisting of 400 genuine and 400 forged signatures. The proposed model achieved an accuracy of 90.5%, precision of 98.8%, recall of 82.0%, and F1-score of 90.0%. The high precision indicates that SS- ANFIS is effective in minimizing false positive predictions, which is important for academic document verification. The results show that the proposed model provides a practical, interpretable, and computationally efficient approach for offline signature verification, particularly in institutional settings with limited labeled data.

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