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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 88 Documents
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Search results for , issue "Vol 15, No 6: December 2025" : 88 Documents clear
Exploring cookies vulnerabilities: awareness, privacy risks and exploitation Hamzah, Nor Anisah Amir; Adnan, Anis Safiyyah; Salleh, Norsaremah
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.pp5792-5803

Abstract

This study investigates cookie vulnerabilities, focusing on awareness, privacy risks, and exploitation techniques. We used a mixed-method approach that combines insights from a survey study and a systematic mapping study of 27 papers from online databases to comprehensively address the research topic. The results show a moderate level of user awareness about cookie-related privacy risks, with significant concerns over user tracking and profiling, identified in 88% of the reviewed studies. Key risks include sensitive data exposure, privacy and consent issues, targeted advertising, ineffective mitigation measures, and cyberattacks. Tracking via cookies, and especially third-party cookies were found to pose the greatest risk to end-users. Their widespread use for cross-site tracking and extensive fingerprinting often occurred without users’ awareness or explicit consent. These insights suggest the need for stricter privacy laws, better practices on cookies, and improved user awareness to mitigate concerning risks.
Exploring feature selection method for microarray classification Akmal, Muhammad Zaky Hakim; Fitrianah, Devi
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.pp5584-5593

Abstract

Effectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data.
Combination of rough set and cosine similarity approaches in student graduation prediction Go, Ratna Yulika; Asianto, Tinuk Andriyanti; Setiowati, Dewi; Meilisa, Ranny; Munthe, Christine Cecylia; Kusumawardhana, R. Hendra
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.pp6001-6011

Abstract

Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the department of computer science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the Case-Based Reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases.
Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture Pawaskar, Praveen; H K, Yogish; B, Pakruddin; Yogish, Deepa
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.pp5667-5678

Abstract

Agriculture is the backbone of the Indian economy, with soil quality playing a crucial role in crop productivity. Farmers often struggle to select the appropriate crop based on soil type, leading to significant losses in yield and productivity. To address this challenge, deep learning techniques provide an efficient solution for automated soil classification. In this study, a dataset of 781 original soil images, including clay soil, alluvial soil, red soil, and black soil, was collected from Kaggle and augmented to 3,702 images to enhance model training. Several deep learning models were employed for soil classification, including pretrained architectures and a proposed model, SoilNet. Experimental results demonstrated that DenseNet201 achieved 100% validation accuracy, ResNet50V2 98%, VGG16 99%, MobileNetV2 99%, and the proposed SoilNet model 97%. The proposed approach outperformed existing work by surpassing 95% accuracy. Additionally, model performance was evaluated using precision, recall, and F1-score, ensuring a comprehensive analysis of classification effectiveness. These findings highlight the potential of deep learning in improving soil classification accuracy, aiding farmers in making informed crop selection decisions.
Computational modelling under uncertainty: statistical mean approach to optimize fuzzy multi-objective linear programming problem with trapezoidal numbers Shrivastava, Arti; Saxena, Bharti; Bhardwaj, Ramakant; Ghosh, Aditya; Narayan, Satyendra
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.pp5708-5716

Abstract

This study presents a comprehensive approach to solving fuzzy multi-objective linear programming problems (FMOLPP) under uncertainty using trapezoidal fuzzy numbers. The authors propose a novel integration of Yager’s ranking method, the Big-M optimization technique, and Chandra Sen’s statistical mean methods to effectively convert fuzzy objectives into crisp values and optimize them. The methodology allows for managing multiple fuzzy objectives by ranking and aggregating them using various statistical means such as arithmetic, geometric, quadratic, harmonic, and Heronian averages. The model is implemented using TORA software and demonstrated through a detailed numerical example. The results validate the robustness and practicality of the proposed approach, showcasing consistent optimal solutions across all statistical methods. This research significantly enhances decision-making processes in uncertain environments by offering a structured, computationally efficient solution strategy for complex real-world optimization problems.
Intelligent control for distributed smart grid: comprehensive system integrating wave, fuel cell, and photovoltaic power generation B S, Manohar; Banakara, Basavaraja
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.pp5119-5129

Abstract

The intermittent supply from renewable energy sources reckons integration of different renewable sources that can provide robust and uninterrupted energy supply to the grid. This paper applies an intelligent control method to such hybrid power generation involving a wave generator, fuel cell, and solar power generator integrated into the distribution power grid. A common DC link that supplies the voltage source converter (VSC) is powered by the output from the hybridized wave, fuel cell and photovoltaic (PV) output. Wave generator uses the rectifier DC-DC converter, PV uses a maximum power point tracking (MPPT)-controlled DC-DC converter and fuel cell uses a DC-DC converter. All DC sources converge at the DC link, connecting to an inverter featuring another voltage source controller for controlled AC voltage. In instances of power unavailability from renewable resources, the fuel cell seamlessly provides power. The inverter controls the integration of power from these sources to the grid and maintains stable DC link voltage due to the dynamic nature of the DQ controller. MATLAB-based simulation is developed for the proposed controller and a comparison between both proportional integral and adaptive neuro-fuzzy inference system (ANFIS) controller in the DC link voltage regulation loop is observed. An ANFIS controller is employed as an alternative to the proportional integral (PI) controller and found that the ANFIS controller outperformed the PI controller in voltage regulation at the DC link.
Power loss reduction and stability enhancement of power system through transmission network reconfiguration Akor, Titus Terwase; Madueme, Theophilu Chukwudolue; Ohanu, Chibuike Peter; Sutikno, Tole
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.pp6012-6026

Abstract

The power network faces several challenges as electricity usage rises and the frequency of partial and total grid disruptions is of great concern. This paper addresses the problem of voltage instability and high-power losses in transmission network, which threatens the stability of the power grid. The MATLAB R2023a/MATPOWER 5.0 is used to develop a model and analyze using the Newton-Raphson load flow method. The analysis reveals a marginal voltage violation at Bus 13 (below 0.95 p.u.). To enhance stability and efficiency, the network was reconfigured using a hybrid whale algorithm and particle swarm optimization (WAPSO) approach, incorporating new transmission lines (5-8 and 13-14) to improve connectivity and reduce congestion. The reconfiguration reduced active power losses by 29.5% (from 36.013 to 25.371 MW) and reactive power losses by 29.8% (from 301.30 to 211.59 MVAr). The system demonstrated first swing stability, with rotor angles remaining below π/2 (1.5669 rad maximum deviation) and fault clearance within the critical clearing time (0.2 s). Optimized exciter gains and a damping coefficient of 1.5 p.u. ensured effective oscillation suppression and stable generator voltages at 1.05 p.u. The hybrid WAPSO approach proved effective in optimizing voltage and rotor angle stability, enabling the network to meet a 24.086 p.u. load demand while enhancing overall grid reliability.
Stability analysis and robust control of cyber-physical systems: integrating Jacobian linearization, Lyapunov methods, and linear quadratic regulator control via LMI techniques Boutssaid, Rachid; Aboulkassim, Abdeljabar; Kririm, Said; Arjdal, El Hanafi; Moumani, Youssef
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.pp5276-5285

Abstract

Stability issues in cyber-physical systems (CPS) arise from the challenging effects of nonlinear dynamics relation to multi-input, multi-output systems. This research proposed a robust control framework that combines Jacobian linearization, Lyapunov stability analysis, and linear quadratic regulator (LQR) control via linear matrix inequalities (LMIs). The robust methodology does the following: it applies linearization on the dynamics of the CPS; it establishes the stability of the system using Lyapunov functions and LMIs; and it designs an LQR controller. The proposed framework was validated through a comparison between the behavior of a linearized and nonlinear model. The autonomous vehicle application showed: a settling time of 20 seconds; an overshoot of 3.8187%; and a steady-state error of 2.688×10⁻⁷. The proposed framework is robustly demonstrated and has applications to areas in automation and smart infrastructure. Future work includes optimizing the design of weighting matrices and developing adaptive control features.
Citizens’ electronic satisfaction factors in electronic government services: an empirical study from Kuwait Alshehab, Abdullah; Alfayly, Ali; Alazemi, Naser
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.pp5690-5698

Abstract

This study investigates the dimensions of service quality provided by Kuwait’s “Sahel” electronic government (e-government) application and their impact on user satisfaction among citizens and residents. Adopting a quantitative methodology based on the modified electronic government quality (e-GovQual) model, data were collected from 1,064 respondents over four weeks, assessing user experiences across usability, reliability, responsiveness, security, and efficiency dimensions. Results indicate moderate overall satisfaction, with particularly high ratings for transparency and ease of use, yet notable concerns regarding trust and data security. Satisfaction with reliability and technical support was moderate, signaling areas for improvement. The study recommends enhancing the user interface for intuitive navigation, improving real-time data synchronization between governmental entities, providing efficient technical support, and strengthening security measures to build user trust. These recommendations are crucial for advancing Kuwait’s e-government effectiveness. Future research should explore causal relationships among service quality dimensions and incorporate technical assessments by information and communication technology (ICT) experts to further enhance user satisfaction.
Explainable fault diagnosis using discrete grey wolf optimization algorithm for photovoltaic system Hassina, Slimani; Ouahiba, Chouhal; Yassine, Beddiaf; Rafik, Mahdaoui; Hichem, Haouassi; Roumaissa, Hamdi
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.pp5286-5296

Abstract

The present article introduces the discrete grey wolf optimization algorithm (DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA integrates discrete optimization techniques with explainable artificial intelligence (XAI) methodologies. This approach aims to overcome limitations associated with traditional fault diagnosis methods, such as limited accuracy in identifying complex patterns and low interpretability. Furthermore, it mitigates early convergence problems commonly encountered in optimization algorithms and enhances adaptability to discrete classification challenges. The DGWOA algorithm is designed to generate interpretable classification rules for fault detection through a stochastic search strategy. The explainability provided by the model not only enhances decision-making transparency but also improves diagnostic efficiency and predictive accuracy. The proposed algorithm was evaluated using a photovoltaic system dataset and benchmarked against established rule-based classifiers. DGWOA consistently achieved a classification accuracy of 99.48% and a precision of 100%, demonstrating its effectiveness in enhancing fault detection. Moreover, the interpretability of the generated classification rules contributes to the generation of outcomes that are both actionable and comprehensible to decision-makers.

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