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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 48 Documents
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Search results for , issue "Vol 16, No 3: June 2026" : 48 Documents clear
Analytic algebraic Riccati solution for a robust control system: application to 2-DOF arm robot Meriem, Menad; Zoubir, Ahmed Foitih; Abdellah, Mokhtari
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.pp1159-1174

Abstract

An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonality process. An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonalization process. The applied method is used to improve robust control of second and third-order state-dependent systems by handling nonlinearities. An H∞ controller is designed in this context via backstepping technique to enhance robustness and reduce computational effort. The effectiveness of this method has been demonstrated on a two-degree-of-freedom (2-DOF) robotic manipulator arm. Simulation results validate the performance of the controller, showing improved tracking accuracy, disturbance rejection, and overall system stability, thereby confirming the efficiency and applicability of the combined analytic Riccati algebraic equation and H∞ backstepping approach for nonlinear robotic systems.
Sepsis detection using biomarkers and machine learning Vu, Tuan Anh; Bac, Dang Hoai; Nguyen, Minh Tuan
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.pp1286-1297

Abstract

Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Integrating BERT fine-tuning and genetic algorithm for superior depression detection in social media Aouragh, Abd Allah; Bahaj, Mohamed; Toufik, Fouad
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.pp1474-1484

Abstract

Early detection of depression is crucial for minimizing its adverse effects on mental and physical health. Recent advancements in natural language processing facilitate the large-scale analysis of social media texts to identify depressive tendencies. Our study introduces a novel approach by integrating a genetic algorithm for hyperparameter tuning, optimizing the classification performance beyond conventional methods. We provide a comprehensive comparison of vectorization techniques, including term frequency-inverse document frequency (TF-IDF), Word2Vec, and a fine-tuned bidirectional encoder representation from transformers (BERT) model specifically adapted to our dataset. Using a dataset of 7,731 entries, we implemented standard pre-processing steps such as stop word removal and lemmatization before vectorizing the text. Five machine learning algorithms—decision tree, logistic regression, random forest, gradient boosting, and support vector machine—were evaluated, with hyperparameter tuning performed using a genetic algorithm. The highest accuracy (95.99%) and F1-score (95.91%) were achieved with the combination of fine-tuned BERT, support vector machine, and genetic algorithm optimization. This study demonstrates the advantages of integrating BERT fine-tuning with genetic optimization, outperforming traditional TF-IDF and Word2Vec approaches in depression detection.
Retrieval-augmented generation in enterprise knowledge systems: architecture, benefits, and applications Baqar, Mohammad
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.pp1407-1416

Abstract

This paper presents an adaptive retrieval-augmented generation (RAG) framework for enterprise knowledge systems that combines multi-source ingestion, semantic indexing with Hugging Face embeddings and Facebook artificial intelligence similarity search (FAISS), metadata-aware retrieval, and grounded large language model generation. The research addresses a persistent enterprise gap: critical knowledge is distributed across documentation, tickets, code repositories, and collaboration tools, while static keyword search and periodically retrained language models cannot keep pace with rapidly changing operational data. The proposed approach contributes a privacy-preserving architecture, a retrieval-and-feedback loop that improves ranking quality over time, and a unified workflow that links evidence retrieval to solution recommendation. In an evaluation over a 1.2 million-document corpus and a six-week pilot, the framework improved Precision@10 from 0.58 to 0.81, reduced documentation retrieval latency from 45.6 s to 12.3 s, and shortened average bug-resolution time from 18.4 h to 7.2 h. These findings indicate that enterprise RAG can materially improve troubleshooting speed, knowledge reuse, and decision support while maintaining stronger control over sensitive organizational data. The broader implication is that adaptive, governed RAG systems can serve as a practical foundation for future enterprise artificial intelligence (AI) assistants, analytics platforms, and compliance-aware decision workflows.
Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS Yusoff, Azizul Mohd; Salam, Sazilah; Mohamad, Siti Nurul Mahfuzah; Saputro, Rujianto Eko
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.pp1319-1328

Abstract

The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.
AI-enabled energy-aware routing approach for future-wireless sensor networks Singh, Shamsher; Kumar, Mandeep
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.pp1543-1561

Abstract

Next-generation wireless sensor networks (WSNs) demand intelligent, energy-aware communication mechanisms capable of sustaining long-term operation in environments with varying conditions and strict resource limitations. Traditional routing protocols often fail to optimize energy consumption under varying network densities, heterogeneous traffic patterns, and environmental uncertainties. This research proposes an AI-enabled energy-efficient routing protocol (AI-EERP) designed to enhance network lifetime, stability, and data delivery performance in next-generation WSNs. The protocol integrates machine learning–based node selection, adaptive clustering, and predictive residual-energy estimation to make optimized routing decisions in real time. Using AI-driven models, AI-EERP dynamically adjusts routing paths based on energy patterns, link quality, and network topology changes. The simulation outcomes clearly indicate that the proposed approach achieves notable gains in energy efficiency, packet delivery reliability, and network lifetime when compared with traditional routing protocols, including LEACH, PEGASIS, and HEED. The proposed approach establishes a robust and scalable framework for future intelligent WSN deployments across applications including smart cities, precision agriculture, environment-focused applications and automated industrial operations.
Smart water distribution for smart cities based on Internet of Things Douli, Amal; Benahmed, Khelifa; Draoui, Belkacem
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.pp1655-1668

Abstract

Against an unprecedented water crisis in our country, balancing water supply and demand is necessary for a secure and sustainable water supply. This challenge requires systems capable of delivering the necessary quantities while conserving resources. Numerous research initiatives focus on addressing water distribution challenges with the help of smart water systems to optimize network operations and minimize water demand. Based on these advancements, this paper proposes a new smart water distribution system for southwest of Algeria. The system integrates the Internet of Things (IoT), information and communication technologies, and smart technologies to address critical attributes for enhancing efficiency. To achieve the efficient management of two-way flows (both water and data) based on water demand and its availability, two innovative architectures have been proposed, using various measurements of water quantity and quality parameters. Algorithms to automate and optimize water distribution are also proposed. According to obtained results, performance has improved, with an accuracy rate of over 98%. These results establish the suggested system as a strong option for intelligent and sustainable water resource management by demonstrating its efficacy and durability.
Optimization of transfer learning for facial emotion classification on the FER-2013 dataset Barkah, Nida Muhliya; ‘Uyun, Shofwatul
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.pp1213-1226

Abstract

Facial expressions play a key role in non-verbal communication by naturally reflecting human emotions. Facial emotion recognition (FER) using computer vision has gained attention with advances in deep learning. However, deep learning models require large datasets to perform well, posing a challenge for FER tasks with limited data. Transfer learning is a promising approach to address this issue, but a standardized method for FER is yet to be established. This study optimizes three transfer learning models ResNet-50, Inception V3, and Xception on the FER-2013 dataset. Experiments include testing input image sizes, hyperparameter tuning, data augmentation, layer addition, and training methods. Results show each model requires different input sizes for best accuracy. Hyperparameter tuning improves accuracy by 6.35%, 4.69%, and 1.04% for ResNet-50, Inception V3, and Xception, respectively. Augmenting only the disgust class yields better accuracy than augmenting all classes. The freeze fine-tuning method is less effective than fine-tuning alone on datasets with thousands of samples but outperforms the freeze layer method. The best accuracies achieved are 64.89% (ResNet-50), 65.83% (Xception), and 66.40% (Inception V3). These findings provide insights into freeze fine-tuning limitations and guidance for optimizing transfer learning in FER with limited data.
Designing and evaluating a community-based digital dictionary system for the Balinese language: An IT innovation adoption study Pramartha, Cokorda; Purnama, Madek Jeani; Sarasvananda, Ida Bagus Gede; Arka, I Wayan; Watiniasih, Ni Luh
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.pp1369-1381

Abstract

Regional and vulnerable languages increasingly depend on digital tools to remain visible and usable in everyday life, yet many dictionary initiatives are described mainly in terms of content or interface features rather than evaluated as information-system innovations. This paper presents an exploratory design science study of a community-based Balinese digital dictionary that supports bidirectional Balinese-Indonesian lookup, Latin and Balinese Unicode script, speech-level information, part-of-speech tagging, related-word search, and role-based contribution workflows. The platform is implemented as a web-based system with a three-tier architecture and relational database. To evaluate adoption readiness, 40 users completed representative tasks and then responded to an adapted Moore and Benbasat IT innovation adoption instrument covering seven constructs. The results show high ease of use, relative advantage, and compatibility, indicating strong functional value and fit with user routines. Image and visibility are moderate, while result demonstrability and visibility show lower reliability and are therefore interpreted as exploratory indicators. The study contributes both a documented digital-dictionary artefact for Balinese language support and a reusable evaluation approach for other early-stage community-facing information and communication technology (ICT) systems. The findings suggest that wider uptake depends not only on technical quality, but also on institutional visibility, outreach, and continued content enrichment.
An internet of things-telemedicine platform empowered by 5G mobile networks for Tunisian Rural places Monia, Ibrahim; Bechir, Dadi Mohamed; Chibani, Rhaimi Belgacem
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.pp1261-1271

Abstract

With the advent of Internet of Things (IoT) technologies, offering new possibilities for remote healthcare delivery, the medicine sector has undergone significant advancements in recent years. New tools are used, and diagnostics have become more accurate. We suggest creating a platform that can be extended for several applications. This platform has been realized to attest and demonstrate how IoT technology offers devices that could be integrated to provide novel services like remote consultations. Our proposed platform contains novel functionalities such as real-time video calls, instantaneous messaging, live notifications, vital signs monitoring, and electronic health record access. This is accomplished with enhanced qualities of remote healthcare services. Added to this, healthcare access equity will be guaranteed. The paper emphasizes the potential of Laravel 11 as a framework offering powerful features for creating modern and high-performance applications. We have integrated Laravel Reverb, a powerful real-time communication package, to provide seamless real-time communication with users. With our application, notifications and interactions are dynamically created. This allows instant updates to delivery and engages the user experience. The database was designed based on the latest version of MySQL 8, coupled with the advanced capabilities of PHP 8.2. This combination provides unparalleled performance, scalability and reliability. Added to that, IoT’s technology usage helps to improve healthcare access and delivery, especially in underserved areas. Human and machine cooperation is a main factor of the 5th industry level. This is widely respected by our platform. This offers great help, especially for those isolated and underserved areas, as we hope.

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