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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 3,049 Documents
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Design and analysis of peaking capacitor for generation of high voltage fast risetime pulses Palati, Madhu; Pattabhi, Manjunatha Babu; Dsouza, Ozwin Dominic
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10297

Abstract

High voltage pulses of short duration are required in certain applications, here precise and rapid electrical energy delivery is required. These include applications like pulsed power system, high speed switching circuits, plasma generation circuits, high speed imaging, non-thermal food processing. One of the best and cheap methods of producing the high voltage fast rise time pulses is using Peaking capacitor. Designing a high-voltage peaking capacitor requires careful consideration of various factors such as voltage rating, capacitance value, energy storage capacity, physical size, dielectric material, and safety measures. In this paper, designing of 200 pF, 300 kV peaking capacitor is presented and simulation is carried out in PSPICE. The maximum peak voltage across the peaking capacitor, the time to reach peak voltage, and the maximum peaking current obtained are 290 kV, 98.9 ns, and 22.41 kA, respectively. Also, to study the electric field of the medium used in peaking capacitors, Quick field software is used. The maximum electric field in Perspex, used as a dielectric medium, was 13 kV/mm.
Modified integral sliding mode speed controller for predictive stator flux and torque controlled induction motor drive Vo, Hau Huu; Tran, Dat Vinh Phat
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11081

Abstract

The paper deals with utilization of integral sliding mode (SM) in speed controller of predictive torque and flux control (PTC) of induction motor (IM). At first, the model-based prediction of the motor torque and stator flux is presented. Then, the voltage vector of the inverter is selected to minimize the errors of the torque and the flux. The proportional-integral (PI) speed controller is used to provide the reference torque for the torque controller. However, the PI speed controller is not achieved high performance especially in cases of high load torque and low speed commands. Then, the integral SM one is employed to enhance the speed control performance. Simulations of two speed controllers in PTC drive are carried out. Evaluations using criterions relevant to speed error confirm the expected performance of the integral SM speed controller.
An innovative deep learning approach for Arabic race recognition Saif, Amal; Ibrahim, Rahmeh; Alnagi, Eman; Ahmad, Ashraf; Aref, Abdullah
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10851

Abstract

In computer vision, human race detection has become a critical application across many domains, such as security and customized marketing. Deep learning approaches, such as convolutional neural network (CNN), have played an essential role in improving human race detection. Nevertheless, detecting Arabic race is still a field that has received little attention. In this paper, an Arabic human race dataset comprising the following classes: Gulf, Levant, Sudan, Egypt, and North Africa (excluding Egypt) has been collected and proposed as a starting point for Arabic race classification. This dataset has been evaluated using a simple CNN-based model and other transfer learning models: DenseNet121, VGG16, and ResNet50. The difficulty in classifying these regions lies in the similarity of border areas in people’s features and in intermarriage between different regions, which helps transfer genetic traits that distinguish one region from another. The best results in recall, F1-score, precision, and accuracy were obtained by the DenseNet121 model, which achieved an average accuracy of 0.746 across five folds.
Object detection for waste management: a comparative review of models, challenges, and future directions Tamin, Owen; Moung, Ervin Gubin; Farzamnia, Ali
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10696

Abstract

Despite growing interest in automated waste detection, existing surveys either focus on a narrow set of models or lack systematic comparisons across object detection paradigms. This review addresses that gap by examining recent advances in deep learning for waste management, spanning two-stage detectors (Faster region-based convolutional neural network (Faster R-CNN) and Mask region-based convolutional neural network (Mask R-CNN)), single-shot frameworks (you only look once version 1 (YOLO)v1 to YOLOv11), and emerging Transformer-based models (ViT-WM and AL-DETR). Faster R-CNN achieved category-level accuracy of 91.68% and overall accuracy of 89.68%, while Mask R-CNN reported AP values between 26.2% and 34.5% across varied datasets. YOLO models demonstrated strong real-time capability, with YOLOv5 reaching a mAP@0.5 of 92.96% and YOLOv8 achieving 97.63% accuracy with precision and recall above 93%. Transformer-based approaches are especially promising: ViT-WM achieved 98.17% accuracy, the highest among reviewed models, and AL-DETR reported a mAP of 58.9% while integrating active learning (AL) strategies to reduce reliance on extensive labeled data. These results emphasize YOLO’s efficiency for real-time waste sorting and the potential of Transformer architectures for handling complex, cluttered environments. Remaining challenges include dataset variability, computational demand, and limited standardized benchmarks. Future research should prioritize developing comprehensive datasets, optimizing Transformers for real-time use, and leveraging AL to enhance generalizability with reduced annotation effort.
FA-optimized fractional order PI control for bidirectional V2G and G2V systems with integrated subsystem management Ismail Mujupur, Mohamed Shiek Mothi; Mohamed Mustafa, Mohamed Ismail
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10895

Abstract

This paper presents a Firefly algorithm (FA)-optimized, fractional order proportional-integral (FOPI), control strategy for bidirectional vehicle-togrid (V2G), and grid-to-vehicle (G2V) systems. The optimization focuses on minimizing key performance indices—integral of time-weighted absolute error (ITAE), integral of squared error (ISE), and integral of absolute error (IAE)—to achieve optimal dynamic response and energy efficiency. The control system incorporates converter with battery controller. The FA is employed to fine-tune the FOPI parameters, leveraging its robust global search capability to balance trade-offs between fast response, minimal overshoot, and stability. Simulation results validate the effectiveness of the proposed approach, demonstrating superior performance compared to conventional proportional–integral (PI) controllers, with ITAE reduced by 63.4% compared to PI controller. This study provides a scalable and efficient solution for advanced energy management in V2G/G2V systems, contributing to the sustainable integration of electric vehicles (EVs) with smart grids.
Hybrid machine learning model towards neuroimaging analysis for detection and grading brain tumors Munirathnam, Lakshminarayana; Shivaswamy, Rashmi; Prakash, Suraksha; Shafi, Reshma; Kumar, Karanam Sunil
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10988

Abstract

Neuroimaging analysis enables detailed observation of brain tumors, with growing adoption of advanced imaging techniques in clinical practice. Limitations of conventional approaches in supporting proactive decisionmaking and reliable grading are increasingly addressed through machine learning. However, earlier models often faced challenges of limited generalization and computational burden. To overcome these issues, this study introduces a hybrid convolution neural network–support vector machine (CNN–SVM) framework that combines ResNet-50 feature extraction with a feature weighting (FW) strategy and SVM-based classification for improved diagnostic precision. The system is further enhanced with a clinically guided grading scheme, mapping classification outputs into malignant, benign, and healthy categories for greater interpretability. The proposed model was evaluated on three benchmark neuroimaging datasets (Figshare, Kaggle magnetic resonance imaging (MRI), and BraTS-2019) and achieved up to 98.6% accuracy with high sensitivity and specificity, while retaining low computational cost and rapid inference, outperforming conventional CNN-only methods.
Two-stage random search–Bayesian optimization for CNN-based short-term load forecasting Nguyen, Tuan Anh; Ngoc Tran, Thanh
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11030

Abstract

This study proposes a two-stage hyperparameter optimization pipeline for convolutional neural network (CNN)–based short-term electricity load forecasting. In the first stage, random search is used to broadly explore candidate configurations, including the number of filters in each convolutional layer, batch size, training epochs, and the loss function. In the second stage, Bayesian optimization based on the tree-structured Parzen estimator (TPE), implemented in Optuna, refines promising regions of the hyperparameter space to obtain a better-performing model. The optimized CNN is evaluated using half-hourly (30-minute) electricity demand data from New South Wales (NSW), Victoria (VIC), and Queensland (QLD), and is benchmarked against a baseline CNN, a multilayer perceptron (MLP), an extended short-term memory network, and single-stage optimization variants. Across the three regions, the proposed approach achieves mean absolute percentage error (MAPE) values between 1.05% and 1.14%, representing an improvement of approximately 58% over the baseline CNN. Statistical robustness is examined using paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on per-timestamp errors. Overall, the results indicate that combining random search with Bayesian optimization improves CNN forecasting accuracy across the three studied regions and provides a transparent tuning framework for future replication.
Moderate guerrilla for the user’s understanding of the artificial intelligence-based user security awareness prototype I Putu Agus Eka Pratama; I Made Oka Widyantara; Linawati Linawati; Nyoman Gunantara
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10478

Abstract

To enhance user security awareness in response to the numerous cyberattacks targeting users, we developed an artificial intelligence (AI)based prototype utilizing the naive retrieval augmented generation (RAG) method. In pursuit of this objective, we conducted user testing employing the usability testing method to evaluate users' comprehension of the developed prototype. We integrated moderate and guerrilla techniques in usability testing by engaging 20 randomly selected respondents from the government, private sectors, and industries. The majority of participants were male, aged 26-35 years, holding a bachelor's degree, and possessing 510 years of computer experience. The test data were analyzed using the USE assessment matrix, which includes four assessment parameters: usefulness, satisfaction, ease of use, and ease of learning (USE). The data were presented in tabulated form, with total and average values. The test results indicate that usefulness, satisfaction, ease of USE achieved a total value exceeding 4.00 and an average value of 4.29, within an interval range of 4.20-5.00, categorized as very good. The findings of this study have implications for enhancing user security awareness and provide feedback for refining the framework and prototype in subsequent research.
Optimizing the economic evaluation method for calculating the total owning cost of induction motors Agha, Ayman; Alzaareer, Khaled; Alkashashneh, Hudefah
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10608

Abstract

This paper presents an approach for estimating the total ownership cost (TOC) of induction motors (IMs) by applying five different economic evaluation methods, each based on specific assumptions. These methods are examined and compared in detail to demonstrate the difference in their implementation and outcomes. In addition to the technical data of the analyzed IM, the methods also consider significant economic factors that affect TOC assessments, such as electricity tariffs, initial investment cost, operating hours, discount rate, and the anticipated lifespan. The proposed approach offers a practical tool to support decision-making when selecting the most economical among different alternatives offered by various manufacturers or venders. A numerical example is presented to demonstrate the application of the proposed work, including detailed calculations with results summarized in tables. The paper concludes by offering practical guidance on selecting the most appropriate economic method for the evaluated scenarios of IM, offering direction to site engineers and decision- makers in balancing technical aspects with long-term economic considerations. The calculation is straightforward and can be performed using standard mathematical tools, illustrating how various factors influence costs and enhancing the reliability of the result. For large-scale analyses, MATLAB provides additional computational efficiency and scalability.
Adaptive bidirectional heuristic rapidly exploring random tree* for efficient path planning Suwoyo, Heru; Faudzi, Ahmad 'Athif Mohd; Adriansyah, Andi; Gunardi, Yudhi; Andika, Julpri; Tian, Yinzhong
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11859

Abstract

Sampling-based path planning algorithms such as rapidly exploring random tree* (RRT*) are widely used for autonomous navigation in complex environments. However, many RRT variants suffer from slow initial exploration, suboptimal convergence, and search inefficiency in dense spaces. Based on this, adaptive bidirectional heuristic-RRT* (ABH-RRT*) is proposed. It is a novel method introduced as a unified path planner. ABHRRT* integrates bidirectional tree growth, heuristic-based parent selection, fast-informed hybrid sampling, and adaptive reordering to improve exploration efficiency and path optimality. The algorithm speeds up the initial path recovery caused by the presence of dual tree expansion and fast sampling. In addition, the algorithm also refines the solution using informed sampling and adaptive reordering to improve convergence toward near-optimal paths. The performance of ABH-RRT* is evaluated in four environments with different complexity levels and compared with RRT, RRT*, Fast-RRT*, Smart-RRT*, and Informed-RRT*. Experimental results show that ABH-RRT* consistently produces shorter paths and faster convergence, reduces path cost by 2–24% and increases convergence speed by 40–58% in dense and constrained environments. These results show that ABH-RRT* is a better and adaptive solution for path planning in complex scenarios.

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