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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 6,393 Documents
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Elitist genetic algorithm improved with parenting fitness parameter Mustapha, Ouiss; Abdelaziz, Ettaoufik; Abdelaziz, Marzak
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.pp883-894

Abstract

In genetic algorithms, the selection of individuals that will be part of future generations is a critical process of the algorithm. Various strategies exist to select these individuals: the general approach and the elitist approach. The general approach involves replacing the whole current population with the offspring generated so far. The elitist approach introduces a competitive element in which both parents and offspring compete for survival, and only fit individuals will be part of the next generation. While selecting fit individuals helps the algorithm to produce better results, the elitism has a major drawback: the premature convergence, which can limit the algorithm's overall performance. In this article, we compared a typical elitist genetic algorithm and an elitist algorithm improved with the parenting fitness parameter in resolving the vehicle routing problem with drones (VRPD). The parenting fitness parameter helps preserving diversity by retaining parents with high offspring potential despite of their personal fitness. The findings from the study demonstrates that integrating the parenting fitness parameter lead to better results in comparison with a typical elitist genetic algorithm, with relative improvement varying from 1.06% to 10.34% according to the dataset’s size.
Single-stage single-phase grid connected inverter proportional resonant and maximum power point tracking controllers for enhanced photovoltaic system performance Kabba, Abdelaziz; Lassioui, Abdellah; El Fadil, Hassan
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.pp651-662

Abstract

The paper develops a current control methodology for a single-phase grid-tied DC/AC inverter applied to photovoltaic (PV) energy conversion systems. It incorporates an algorithm for finding the optimal voltage and current points to obtain maximum power point tracking (MPPT), the purpose of which is to ensure better energy extraction. This is followed by a proportional-integral (PI) controller to generate the reference current. In addition, a proportional-resonant (PR) controller is used to infinitely amplify the fundamental frequency signal, which makes it possible to eliminate the steady-state error. The analytical foundations of the PR controller are presented and substantiated through simulation studies implemented in MATLAB/Simulink. The phase-locked loop (PLL) is used for synchronization, enabling accurate phase detection of the grid voltage for effective power injection. An LCL filter is also implemented between the inverter and the grid. The results provided by the dedicated software confirm the effectiveness of the proposed control system.
Optimizing neural networks: a comparative study of activation functions in deep learning Mobarki, Ahmed; Sheikh, Abdullah
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.pp945-963

Abstract

Activation functions play a pivotal role in deep learning (DL) models, thus shaping their learning capabilities, convergence behavior, and generalization performance. However, the selection of activation functions without systematic evaluation in many applications has limited the model's performance. Inappropriate activation functions may cause gradients to shrink or blow-up during backpropagation, thereby affecting effective learning. To conquer this problem, this paper provides a novel comprehensive empirical investigation of nine activation functions, including traditional functions like rectified linear unit (ReLU), Sigmoid, Tanh, and ELU, and modern nonlinearities like Swish, Mish, GELU, and SMU. In the proposed methodology, these nine activation functions are evaluated within two prominent neural network architectures, namely convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs), across benchmark datasets, namely CIFAR-10, CIFAR-100, and MNIST. The evaluation criteria include validation accuracy, loss, training time, and gradient stability. Experimental results proved that GELU activation function improved MLP accuracy to 98.03% and CNN accuracy to 93.82% while maintaining stable gradients and low loss values of 0.088 and 0.221, respectively. These findings provided practical guidelines for selecting activation functions suited to specific task complexities and model depths, contributing to the design of more efficient and accurate DL systems.
Optimizing usability of electric wheelchairs with voice user experience for acceleration wheel rotation design by the kinematics method Santiyasa, I Wayan; Swamardika, Ida Bagus Alit; Suhartana, I Ketut Gede; Putra, I Gusti Ngurah Anom Cahyadi
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.pp739-752

Abstract

Individuals with quadriplegia experience total paralysis of all four limbs due to spinal cord injuries, leaving them unable to operate conventional electric wheelchairs that rely on joystick control. Existing alternative interfaces, such as head motion and eye-gaze sensors, are often cost-prohibitive and fail to deliver the maneuverability and accuracy required for daily use. Voice recognition emerges as a practical solution because speech ability is typically retained in quadriplegia, offering a hands-free, intuitive control method. This study proposes an electric wheelchair system integrating voice user experience (VUX), machine learning (ML), and kinematics-based wheel rotation control to address these challenges. Voice commands are processed using natural language processing (NLP) for word recognition and support vector machines (SVM) for amplitude classification to dynamically adjust speed and direction. Forward and inverse kinematics optimize wheel rotation angles, ensuring smooth and precise navigation even in constrained spaces. Experimental results demonstrate 92.82% word recognition accuracy and 94.48% accuracy in frequency and amplitude detection. Functional testing recorded average speeds of 0.343 m/s (no load) and 0.305 m/s (with 60 kg load). Usability testing with 15 quadriplegic users reported 93%.
Multimodal machine learning framework for fake review detection R., Rashmi; T., Shobha; C. S., Dhanushree; Santi, Gayatri S.; Devadig, Jeevita S.; L. V., Harshitha
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.pp991-1001

Abstract

Online reviews significantly influence consumer decision-making, yet their credibility is increasingly undermined by the rise of fake and manipulated content. This study addresses the growing challenge of detecting deceptive online reviews by developing a highly accurate, robust, and explainable machine learning framework that supports trust and reliability in digital marketplaces. The proposed multimodal framework integrates textual, behavioural, temporal, and network-based features to enhance detection performance. Textual characteristics are extracted using term frequency-inverse document frequency (TF-IDF) and sentiment analysis, while behavioural and temporal attributes model reviewer activity patterns. Network-oriented features capture suspicious reviewer interactions. To mitigate class imbalance, synthetic samples are generated using the synthetic minority over-sampling technique (SMOTE). Several machine learning models—including logistic regression, decision trees, XGBoost, and a stacking ensemble—are trained and evaluated. Experimental findings show that XGBoost and the stacking ensemble deliver strong balanced performance, achieving an F1-score of approximately 0.87 and an accuracy of 0.94. Decision Trees exhibit high precision (0.98), albeit with comparatively lower recall. To ensure transparency and interpretability, Shapley additive explanations (SHAP) are used to analyse model predictions. Results indicate that reviewer connectivity, co-reviewer counts, and sentiment–rating inconsistencies are among the most influential features. Overall, the proposed framework enhances detection accuracy and provides meaningful, explainable insights, making it well-suited for deployment in real-world digital marketplaces. Future work will focus on extending the framework to multilingual datasets and incorporating adaptive learning mechanisms to address evolving deceptive behaviour.
An enhancement of stock price forecasting based on hybrid BiLSTM-Transformer model Vuong, Pham Hoang; Phu, Lam Hung; Duy, Le Nhat; Bao, Pham The; Trinh, Tan Dat
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1298-1306

Abstract

Stock price forecasting presents a challenging problem due to factors like nonlinearity, seasonality, and economic volatility in financial data. Deep learning approaches can handle nonlinearity and complexity of financial data, but they often face limitations in capturing both local and global dependencies. This study introduces a hybrid Transformer–bidirectional long short-term memory (BiLSTM) model to improve stock price forecasting. Our method combines the strength of BiLSTM with the global context understanding of the Transformer by embedding a 1D convolutional layer. The model can efficiently capture short-term and long-term dependencies in stock data. Experimental results on various datasets show that our hybrid model outperforms other well-known models.
A new multiplier less memcapacitor emulator with non-linear applications Basavanna, Suresha; Shankar, Chandra; S. B., Rudraswamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1132-1147

Abstract

This study describes a memcapacitor emulator without a multiplier that make use of second-generation current conveyor (CCII), operational trans-conductance amplifier (OTA) and the fewest possible passive components. The proposed memcapacitor is proved mathematically and verified using several simulation approaches, such as process corner, non-volatile and hysteresis analysis. Also, provided the layout of CCII and OTA as well. The standard CMOS 90 nm technology is used in the Cadence Virtuoso tool to simulate the proposed memcapacitor emulator. This article also includes the use of memcapacitor emulator in the applications of R-C frequency selective network as well as adaptable neuromorphic structure. To investigate the experimental outcomes, an experimental setup was constructed with commercially available integrated circuits (ICs) CCII’s AD844AN and OTA’s CA3080EZ.
Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification Prenata, Giovanni Dimas; Ridho’i, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1562-1575

Abstract

Emotion classification based on electrocardiogram (ECG) signals has attracted increasing attention in affective computing and biomedical signal processing. However, training deep feedforward neural networks (DFFNN) using conventional gradient-based learning often suffers from local minima and slow convergence, particularly when dealing with nonlinear and limited datasets. This study presents a comprehensive performance analysis of single-stage and multi-stage metaheuristic optimization strategies applied to DFFNN for ECG-based emotion lassification in elderly participants. Five models were evaluated: Pure DFFNN, DFFNN optimized using genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and a hybrid multi-stage DFFNN+GA+GWO model. Experimental results from six independent trials demonstrate a substantial reduction in mean squared error (MSE) when metaheuristic optimization is applied. Pure DFFNN produced final MSE values in the range of 0.07462–0.08977, whereas DFFNN+GWO reduced MSE to 0.01894–0.02411. The proposed multi-stage DFFNN+GA+GWO achieved the lowest MSE of 0.014286 in the best run and an average MSE of approximately 0.0212 across trials. Training accuracy improved from 57.14%–66.67% (Pure DFFNN) to 80.95%–85.71% using metaheuristic pproaches. Although testing accuracy remained relatively stable at 33.33%–50.00% due to dataset size constraints, convergence behavior analysis shows that multi-stage optimization enhances stability and reduces oscillatory updates. These findings confirm that multi-stage metaheuristic optimization significantly improves training stability and error minimization in DFFNN models, offering a promising strategy for robust ECG-based emotion classification under small-sample conditions.
Evaluation of machine learning approach in modelling and forecasting real gross domestic product growth: a comparative study Qureshi, Moiz; Ismail, Muhammad; Ahmad, Nawaz; Hussain, Ibrar; Ghoto, Abbas Ali; Vveinhardt, Jolita
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1339-1349

Abstract

This study aims to provide an efficient and accurate machine-learning approach for modelling and forecasting the real gross domestic production (GDP) in the context of Pakistan. The study forecasts Pakistan's GDP growth rate using different forecasting models, such as naïve, seasonal naïve (SNaive), smoothing, and k-nearest neighbors (k-NN). Machine learning algorithms provide additional advice for data-driven decision-making. According to the findings, the k-NN-based forecasting gives minimum mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) compared to the other three models. Economic policymakers can use accurate models to measure significant economic activity and formulate plans. The results indicate that the model produced accurate projections of future GDP levels for Pakistan.
Integrating Sustainable Development Goals into educational information systems: toward a theoretical model for sustainable school management Arinal, Veri; Miswanto, Miswanto; Setiawan, Kiki; Rahayu, Agus Tanti
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1350-1359

Abstract

This research addresses the critical challenge of implementing Sustainable Development Goal (SDG) 4, "Quality education," in Indonesian secondary schools. While national policies exist, schools lack a systematic digital tool to plan, monitor, and evaluate sustainability-based activities against concrete SDG indicators. To bridge this gap, this study employs a six-cycle design science research (DSR) methodology to develop a theoretical model for a sustainable education information system. The model is designed to integrate SDG principles into school management, enabling systematic data handling, adaptive curriculum functions, and real-time monitoring. A web-based prototype was developed using a React.js frontend and Node.js backend and evaluated through a mixed-methods approach. Data from interviews with 15 administrators and surveys of 97 teachers (yielding a usability satisfaction score of 4.34/5) validated the model’s effectiveness in making educational administration more efficient, transparent, and quality-oriented. The resulting artifact serves as a foundational technical and managerial reference for schools, education offices, and policymakers to leverage information technology in fostering a sustainable, participatory learning culture aligned with the SDGs.

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