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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 73 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 73 Documents clear
Surface-mount device design cycle time reduction using hybrid predictive modeling and optimization algorithm Chin Lim, Chiah; Choon Ngo, Hea; Raba’ah Hashim, Ummi; Hilmi Hasan, Mohd
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9058

Abstract

This study develops a hybrid predictive and optimization model for surface-mount device (SMD) design, addressing the extended design cycle times in the semiconductor industry caused by high computational demands. Challenges are tackled effectively through integration of convolutional neural network (CNN) for high-accuracy predictions and simulated annealing (SA) algorithm for optimization of SMD physical parameters. CNN model that trained on Monte Carlo simulation (MCS) data, achieved a predictive accuracy of 99.91% in forecasting SMD design errors. Concurrently, SA algorithm refined design parameters and substantially reducing error rates to nearly zero after 800 iterations. Our results indicate that combining predictive modeling with an optimization algorithm significantly enhances SMD design efficiency, providing a robust tool for mitigating time-to-market risks in semiconductor manufacturing.
Modified multilevel inverter based an active power filter using fuzzy controller for power quality enhancement R. Chavan, Pranita; Rajaram Patil, Babasaheb
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9503

Abstract

Power electronics-based nonlinear loads generate significant current harmonics, adversely affecting the efficiency and reliability of distribution networks. Active power filters (APFs), leveraging power electronics technology, provide an alternative to passive filters in mitigating harmonics. Multilevel inverter-based (MLI) APFs, particularly for high-power applications, offer numerous advantages but often suffer from increased component count and control complexity. In this article, a novel five-level MLI topology is proposed, featuring a reduced number of switches compared to the traditional cascaded H-bridge topology with eight switches. This research reduces system cost and simplifies controller design. To further enhance system performance, a fuzzy logic controller (FLC) is implemented for DC-link voltage control. Harmonics are identified using the instantaneous p-q theory, and switching signals are generated through multicarrier pulse width modulation (PWM) techniques. Study conducted in MATLAB for a single-phase balanced system demonstrate the effectiveness of the proposed topology. Results reveal a reduction in total harmonic distortion (THD) of the source current from 34.15% to 2.31%, meeting the IEEE-519 standard. The findings validate the proposed APF's capability to enhance power quality by mitigating harmonics. By integrating advanced MLI technology with artificial intelligence-based control, this work offers a cost-effective, efficient solution to improve the performance of polluted distribution networks.
Automatic drowsiness detection system to reduce road accident risks Aprilia, Sella Joanita Nur; Fitrianah, Devi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8902

Abstract

Drowsy driving poses a significant risk to road safety, often equated with impaired driving due to its detrimental effects on cognitive function. This study presents a real-time drowsiness detection system utilizing the YOLOv5 algorithm, enhanced with contrast limited adaptive histogram equalization (CLAHE) technique, to improve detection in low-light conditions. The proposed method analyzes visual cues indicative of drowsiness, such as eye closure and head nodding, leveraging advanced computer vision techniques. A dataset was augmented from 1,056 original images to 2,112 images via CLAHE, resulting in significant improvements in model performance. Experimental results indicate that the model achieves a mean average precision (mAP) of 0.959, with precision and recall values of 0.9529 and 0.9528, respectively, underscoring the effectiveness of CLAHE in enhancing image quality and overall detection performance. The application developed from this model provides timely alerts to drivers, aiming to prevent accidents and promote road safety. This research contributes to the advancement of automated safety systems in vehicles, particularly under challenging lighting conditions.
Adaptive micro strip antennas for 5G networks Lakshmi Narayana Sudha, Kanakatte; Pallipadi Jayaprakash, Sapna; Neralahalli Prakash, Deepa
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8993

Abstract

The advent of fifth-generation (5G) technology and progressing further to six-generation (6G) technology has created a new era of high-speed wireless communication, demanding antennas with enhanced capabilities to fulfill the dynamic demands of various applications. This paper presents novel approaches to designing antennas in the GHz frequency range for 5G networks by incorporating re-configurability features. Adaptive antennas provide the flexibility to alter their radiation configurations, frequencies, or polarization states, allowing them to optimize performance under different operating conditions. The theoretical foundations are explored, and reconfigurable antennas are simulated using HFSS, focusing on frequency and pattern variation at GHz frequencies using different types of switches such as pin diodes and rods. Through simulations, the antenna's S parameters are evaluated, demonstrating its capacity to meet the rigorous specifications of 5G applications. Its adaptive nature enhances connectivity and overall network performance, supporting the successful deployment and advancement of 5G technology in diverse real-world applications.
Levels of consciousness in psychopathology according to monitoring of neural network centers alpha rhythm rs-EEG Lytaev, Sergey; Belskaya, Ksenia
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9546

Abstract

Consciousness is the highest mental function that integrates attention, memory, individual experience, emotions, and all modalities of perception, information processing and other manifestations of higher nervous activity of a person. This research was aimed to theoretically substantiate the functional connection of the brain alpha regulatory system with the modulation of conscious activity and to identify pathological electroencephalography (EEG) patterns of the alpha rhythm characterizing a decrease in the level of consciousness. 40 patients (main group) with current symptomatic schizophrenia associated with neurocognitive and depressive symptoms, and 38 healthy subjects (control group) were examined. Both nonspecific physical parameters of the alpha wave process–index, frequency and amplitude, and physiological features of alpha oscillations–regularity, auto rhythmicity (modulation) and stability of the EEG alpha rhythm were analyzed with using WinEEG, EEG Studio, and Loreta-Key viewer programs. A line of indicators for the alpha rhythm in schizophrenia have been calculated–based on coherence in different brain areas, the latent period (LP) of desynchronization, the average number of bursts and the tone of the cerebral cortex.
Internet of things forensic: contemporary issues, challenges, and future research directions Altaha, Safa; M. Hafizur Rahman, M.
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8716

Abstract

The internet of things (IoT) comes with great capabilities as well as new opportunities for attackers and criminals. Even though digital forensics is considered to be mature and has been studied by many researchers in recent years, IoT forensics is relatively new and yet to be thoroughly explored. IoT technology has unique characteristics that require adoption of the traditional digital forensic approaches, frameworks, and tools. The primary goal of IoT forensics is to collect, preserve, and present evidence in a manner that meets legal standards and country law from a specific IoT system that includes connected devices and sensors via different types of networks and associated cloud environments. In this paper, we explored the main difference between IoT forensics and traditional digital forensics. This paper aims to provide a comprehensive and up-to-date overview of recently proposed solutions for addressing the IoT forensic domain. we highlighted the limitations of the recently proposed framework and the utilized technologies by researchers. In addition, we recommended some new research directions that could enhance the IoT investigation process. The goal of this paper is to provide a clear understanding of the currently used technologies and other fields in IoT forensic frameworks, limitations, and directions in this area, which may be helpful for future researchers interested in this field.
Optimal power control for wind/solar hybrid energy system based on multi-objective particle swarm optimization Putri, Ratna Ika; Ronilaya, Ferdian; Syamsiana, Ika Noer; Amalia, Zakiyah; Jasa, Lie
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9350

Abstract

The effectiveness of wind and solar energy as electricity generators is significantly impacted by unpredictable and varied environmental circumstances, which affect the output power of the wind-solar hybrid power generation system. So, a control system is required for the optimal power production of hybrid renewable energy systems (HRES). This study delineates optimal power management in wind/solar hybrid energy systems by the application of multi-objective particle swarm optimization (MOPSO) algorithms, inverter controllers, and battery controllers. The MOPSO algorithm enhances power generation by modifying the duty cycle of the direct current (DC)/DC converter based on the output from the wind turbine and photovoltaic (PV) system. The proportional-integral (PI) controller functions as both an inverter and battery controller to ensure the constancy of the DC link voltage and output power. The efficacy of the developed control was evaluated using simulation. A comparison has been conducted between the efficacy of the MOPSO algorithm and the perturb and observe (PO) approach. The simulation findings indicate that the MOPSO algorithm surpasses the PO method for performance and output power. The output power produced by HRES with the MOPSO algorithm exceeds that of the PO approach. Optimal power control utilizing MOPSO can yield optimal power despite fluctuations in wind and solar intensity.
Hybrid ANN-PSO MPPT with high-gain boost converter for standalone photovoltaic systems Byar, Mohcine; Chbirik, Ghizlane; Brahmi, Abdennabi; Abounada, Abdelouahed
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9451

Abstract

Standalone photovoltaic (SPV) systems play a critical role in delivering clean energy to remote areas; however, maintaining consistent maximum power point tracking (MPPT) under dynamic environmental conditions remains a significant challenge. This paper proposes a hybrid artificial neural network–particle swarm optimization (ANN-PSO) based MPPT algorithm, integrated with a high-gain boost converter (HGBC), to overcome these limitations. The hybrid approach leverages the predictive capacity of ANN and the global optimization strength of PSO to achieve accurate and rapid tracking of the maximum power point under fluctuating irradiance. In addition, the high-gain converter improves voltage amplification and reduces power losses, improving overall system efficiency. The simulation results in MATLAB/Simulink confirm that the proposed system achieves a 99.7% tracking efficiency, faster convergence than conventional MPPT techniques, and significantly reduced power ripple. These results indicate that the proposed strategy can improve energy harvesting and operational stability in SPV applications. In addition, it offers a scalable and cost-effective solution suitable for off-grid electrification, particularly in rural and underdeveloped regions, contributing to global renewable energy goals.
Feature selection for support vector machines in imbalanced data Toleva, Borislava; Ivanov, Ivan; Hooper, Vincent
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9556

Abstract

Addressing the effects of class imbalance on feature selection models has become an increasingly important focus in academic research. This study introduces a novel support vector machine (SVM)-based algorithm specifically designed to handle class imbalance during the feature selection process. Using the Taiwan bankruptcy dataset as a case study, the algorithm incorporates the ExtraTreeClassifier() to manage class imbalance and identify a reduced set of relevant variables. To validate the selected features, SVM is applied within the imbalanced data context. Subsequently, analysis of variance (ANOVA) ranking is employed to further refine the variable set to three key features. An SVM model tailored for class imbalance is then constructed to assess the effectiveness of the final feature set. The proposed model significantly outperforms existing approaches in terms of classification performance. Specifically, it achieves a Type I error of 1.17% and a Type II error of 22.9%, compared to 4.4% and 39.4% reported in prior research. In terms of overall accuracy, our method reaches 83.1%, surpassing the 81.3% achieved by earlier studies. These results demonstrate that the proposed feature selection algorithm not only improves SVM accuracy but also outperforms other feature selection techniques when used in conjunction with SVMs, particularly under conditions of class imbalance.
A single-user electronic ticketing system using ERC-721 protocol for smart contracts Okokpujie, Kennedy; Owivri, Oghenetega; Olusanya, Olamide; Daramola, Samuel; Awomoyi, Morayo E.
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8806

Abstract

Single-user electronic ticketing systems face significant security challenges, including fraud and counterfeiting. While blockchain has been explored for electronic ticketing, existing solutions often remain centralized or focus solely on event-based scenarios, not single-user tickets such as flight, train, bus, big transport schemes, movie tickets, and vouchers. This paper presents a decentralized single-user ticketing system to address this gap by utilizing Ethereum's ERC-721 standard for smart contracts (SC). Transparency and privacy are ensured through asymmetric encryption. Digital signatures validate ticket authenticity, and an innovative ERC-721-based verification process is applied. Leveraging Ethereum's ERC-721 Protocols, digital signatures, and the interplanetary file system (IPFS) for decentralized metadata storage, this paper addresses centralization, security, traceability, and transparency concerns. The SC is integrated into a web application, and empirical analysis based on blockchain metrics demonstrates its performance. Results indicate that the system exhibits an efficient ticket transaction completion time of 19.64 seconds and a mean ticket verification time of 3.17 seconds. The outcome illustrates the efficiency of the system in mitigating fraud, counterfeiting, and security risks in single-user electronic ticketing systems.

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