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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Research on optimal design of surface permanent magnet synchronous generator Hoai Nam, Phan; Duc Chuyen, Tran
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10305

Abstract

The more fossil energy is used, the less this energy source will become because it is not an infinite energy and it pollutes the environment, so there is a need for solutions with new and infinite energy sources such as wind energy. This paper designs and focuses on optimizing a floating magnet synchronous generator (SG) for a wind power generation system using finite element analysis (FEA) with ANSYS Maxwell software. This generator is compared with other types of generators such as squirrel cage induction generator (SCIG), wound rotor induction generator (WRIG), SG, doubly-fed induction generator (DFIG), and switched reluctance generator (SRG). Throughout the analysis and design process, the paper emphasizes the significant benefits of surface-mounted permanent magnet (SPM) motors in increasing efficiency and reliability while reducing supply costs. The research results of the paper aim to demonstrate that SPM can meet the needs of high efficiency and low cost in the industrial and civil fields. The results of this study by the authors will provide new contributions to serve as a basis for the design, manufacture, calculation and control of Halbach permanent magnet (Halbach PM) electric machines based on optimization techniques such as genetic algorithms (artificial intelligence) and sustainable optimization (for electrical equipment).
Memory faults using open and short defect models for nano technology applications Muddapu, Parvathi; Venkatesh, Maddela
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.6189

Abstract

As technology progresses from sub-micron to nanometer scales, memory-based systems are increasingly prone to faults. Consequently, developing robust methodologies to achieve defect-free embedded static random-access memory (SRAM) has become a critical challenge in modern very large scale integration (VLSI) design. Also, the increased integration of layout layers leads to form unknown defects. From the existing literature, observed that huge parametric variation is present whenever technology is changed. This is the key issue addressed in this paper, by representing an analysis on the impact of open and short defect models that uses parasitic extraction method while drawing various fault models. Possible open/short defects between the existing nodes are considered for the development of fault models using 45 nm, 32 nm, and 7 nm technologies. The total number of fault models of both kinds observed are 147. Also observed that besides to the existing faults, few undetectable faults are found named as undefined short faults (USF), undefined write after read fault (UWARF), and few faults with multiple faulty behavior.
Autism detection using facial and motor analysis using machine learning Amirbay, Aizat; Baigabylov, Nurlan; Mukhanova, Ayagoz; Mukhambetova, Kuralay; Zaitov, Elyor; Burganova, Roza; Khusanova, Khayriniso; Akhmedova, Feruza
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10319

Abstract

This paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention.
Multi-feature fusion framework for enhanced image deduplication accuracy using adaptive deep learning Shah, Rahul; Kumar Shrivastava, Ashok
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9119

Abstract

Image deduplication is a critical task in domains such as digital asset management, content-based image retrieval (CBIR), and data storage optimization. This paper presents a novel method for improving deduplication accuracy by integrating multiple feature types. A comprehensive framework is proposed that combines visual, semantic, and structural image elements. The system employs deep learning architectures, including convolutional neural networks (CNNs) and transformers, to extract high-level features, which are fused through an adaptive weighting mechanism that dynamically adjusts based on image content. Experimental results across diverse datasets demonstrate that the proposed multi-feature fusion approach significantly outperforms traditional single-feature methods, achieving an average improvement of 15% in deduplication accuracy. By overcoming limitations in handling complex visual similarities, this study introduces a more robust and efficient solution for image deduplication.
Fast lightweight convolutional neural network for Turkish sentiment analysis Alqaraleh, Saed; Hafiz AbdulHafiz, Abdul
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9960

Abstract

This study presents a fast, lightweight, and high-performing fast convolutional neural network (Fast CNN) model tailored for Turkish sentiment analysis (SA). The agglutinative morphology of Turkish, combined with the limited availability of high-quality linguistic resources, introduces significant challenges for conventional approaches. To address these issues, we propose a streamlined Fast CNN architecture consisting of an embedding layer, global max-pooling, dropout, and fully connected layers. Despite its simplicity, the model outperforms seven state-of-the-art convolutional neural network (CNN)-based systems across four benchmark Turkish sentiment datasets. It achieves an average area under the curve (AUC) of 0.94, representing a 6.8% improvement over the strongest baseline and a gain of over 80% relative to several deeper architectures. In addition to its superior accuracy, the model demonstrates reduced computational complexity, making it well-suited for real-world deployment in resourceconstrained environments. Potential applications include customer feedback mining and digital marketing analytics in Turkish-language domains.
The neural network adaptive behaviour model for localization and speed control in autonomous rescue mobile robot operation Hafizd Ibnu Hajar, Muhammad; Persada Nurani Hakim, Galang; Siti Salamah, Ketty; Septiyana, Diah
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9365

Abstract

In robotic operation, an autonomous operation for a mobile robot is needed to operate smoothly, hence, a control system is needed. Numerous architectures for robotics control systems have been put forth. Regretfully, creating a control system architecture is very challenging and occasionally results in inaccuracy in control. An alternative to conventional mobile robot control has emerged to address this issue: behavior-based control system architectures. This paper addresses the behavior of an autonomous mobile robot (AMR) control system in an outdoor rescue operation. The AMR behavior will be governed by the neural network methods, which are a computational intelligence to generate a dependable control algorithm. The architecture is used to coordinate behavior, especially to localize the victims, and for speed control to find the victim location with fast timing. In localization parameters to find the victim in the disaster area, this neural network adaptive model has the smallest error, which is 3.27, compared with other models such as free space model 43.46, and empirical model 4.735. While in robot speed parameter has a low error value, which is 1.47. With this small error, we can conclude that the neural network adaptive behaviour control architecture model for rescue mobile robot operation has been successfully developed.
New approach to measuring researcher expertise using cosine similarity algorithm and association rules Firdaus, Ali; Stiawan, Deris; Samsuryadi, Samsuryadi; Budiarto, Rahmat
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9506

Abstract

This study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%.
Digital twins and IIoT: comparison of Prometheus and InfluxDB Amirkhanov, Bauyrzhan; Ishmurzin, Timur; Kunelbayev, Murat; Amirkhanova, Gulshat; Aidynuly, Azim; Tyulepberdinova, Gulnur
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9687

Abstract

This article presents a comparative analysis of data monitoring and visualization tools—Prometheus and InfluxDB—in the context of digital twins (DTs) applied to industrial settings. DTs optimize production processes using industrial internet of things (IIoT) technologies. Mathematical models assessed the tools based on response time, resource consumption, throughput, and reliability. Prometheus is better suited for high-frequency monitoring, achieving a response time of 0.01 seconds and processing up to 10,000 metrics per second—10–15% better than InfluxDB. It consumes 1.5 times less memory (100 MB versus 150 MB), making it faster and more resource-efficient. Conversely, InfluxDB excels in long-term storage and analytics, handling up to 8,000 metrics per second with a response time of 0.09 seconds. However, it requires more resources, including higher CPU usage (20% versus 15%). Both tools integrate seamlessly with Grafana for visualization, offering flexibility for real-time monitoring and decision-making. The study provides actionable insights for selecting monitoring systems based on project-specific requirements, highlighting Prometheus’s efficiency in dynamic scenarios and InfluxDB’s strength in analytics-focused tasks.
An optimized deep learning framework based on LEE for real time student performance prediction in educational data Muniappan, Ramaraj; Devi Devarajan, Sowmya; Subbarayalu Ramamurthy, Lavanya; Balakumar, Ayshwarya; Gunaseelan, Prathap; Palanisamy, Shyamala; Selvaraj, Srividhya; Sabareeswaran, Dhendapani; Bhaarathi, Ilango
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9773

Abstract

Predicting student performance in real-time remains a critical challenge in educational data mining (EDM), especially with large, noisy, and high-dimensional datasets. This study proposes an advanced deep learning framework that integrates learning entropy estimation (LEE) with models such as support vector machines (SVM), you only look once (YOLO), recurrent convolutional neural networks (RCNN), and artificial neural networks (ANN) to enhance feature selection and classification accuracy. The framework follows a systematic pipeline involving data preprocessing, LEE-based feature extraction, and model training on a real-time academic dataset comprising student demographics, attendance, and performance metrics. Among the proposed models, the LEE-based YOLO (LBYOLO) achieved the highest testing accuracy of 93% and the fastest execution time of 1.84 seconds, while the LEE-based ANN (LBANN) demonstrated consistent performance across precision, recall, and F1-score. The results confirm the superiority of deep learning methods over traditional machine learning techniques for educational prediction tasks. This approach enables early detection of at-risk students and supports timely, data-driven educational interventions. Future work will focus on adaptive learning systems and multi-platform student behavior analysis to support personalized education strategies.
Few-shot brain tumor classification: meta- vs metric-learning comparison Akhmetzhanova, Shynar; Serek, Azamat; Kashayev, Ruslan; Kozhamuratova, Aizhan
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10706

Abstract

Medical imaging requires accurate brain tumor recognition because precise classification is essential for early diagnosis and effective treatment planning. A major challenge in medical applications is that deep learning models typically require extensive amounts of labeled data to perform well. To address this, this research evaluates three few-shot learning (FSL) approaches-prototypical networks, Siamese networks, and model-agnostic meta-learning (MAML)-for brain tumor classification using the Figshare brain tumor dataset. The results show that prototypical networks consistently outperform the other approaches, achieving 89.07% accuracy (95% CI: 88.12–89.96%), 88.73% precision, and 88.67% recall, making them the optimal solution for this task. Siamese networks achieve 83.73% accuracy (95% CI: 82.64–84.76%), while MAML demonstrates significantly reduced performance, with 43.70% accuracy (95% CI: 42.10–45.22%). This study demonstrates that FSL can be applied effectively for medical image classification, with prototypical networks achieving the best performance in brain tumor detection. The inclusion of confidence intervals further validates the robustness and reliability of the results. Future research will focus on improving feature representation and exploring hybrid approaches to better handle rare tumor classes, thereby enhancing the clinical applicability of FSL models.

Page 273 of 291 | Total Record : 2901

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