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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 2,976 Documents
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Comparative analysis of ensemble learning algorithms in enhanced confidence-based assessments Azharludin, Nur Maisarah Nor; Samah, Khyrina Airin Fariza Abu; Dzulkalnine, Mohamad Faiz; Fadzil, Ahmad Firdaus Ahmad; Jono, Mohd Nor Hajar Hasrol; Riza, Lala Septem
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9935

Abstract

This paper provides a comparative analysis of ensemble learning (EL)algorithms to enhance the confidence-based assessment (CBA) in evaluating student performance. Traditional CBA often suffers from misclassification caused by overconfidence and underconfidence, limiting its accuracy and fairness. To address these challenges, an enhanced CBA-EL model integrating bagging and boosting ensemble algorithms is proposed. Five bagging algorithms, which are random forest (RF), decision tree (DT)support vector machine (SVM), K-nearest neighbors (KNN), Naïve Bayes(NB), and four boosting algorithms, which are adaptive boosting(AdaBoost), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were evaluated using a dataset of 276 responses collected from Pre- and Post-Quiz CBA in a discrete structures course. Algorithm performance was evaluated using accuracy, correlation, weighted mean precision (WMP), and weighted mean recall (WMR). RF achieved 73.19% accuracy, 0.725 correlation, 0.751 WMP, and 0.766 WMR, while CatBoost outperformed all with 86.23% accuracy and the highest correlation, WMP, and WMR values, with 0.842, 0.843, and 0.862, respectively. The findings indicate that integrating EL into CBA improves prediction accuracy and supports bias-aware student evaluation. This research advances reliable assessment practices and informs the development of adaptive learning systems.
Embedded deployment of traffic sign detection and recognition systems Taouqi, Imane; Lamane, Mohamed; Klilou, Abdessamad; Arsalane, Assia; Chaji, Kebir
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11215

Abstract

Traffic sign (TS) detection and recognition are essential components of advanced driver assistance systems (ADAS), contributing to safer and more reliable driving. However, deploying deep learning–based vision models on embedded platforms is challenging due to constraints in computational power and energy consumption. In this work, a comparative deployment of you only look once version 7 (YOLOv7) and YOLOv7-tiny deep learning algorithms is conducted on embedded NVIDIA platforms, namely Jetson Nano and Jetson Xavier NX, to evaluate their suitability for real-time TS detection. Following the detection stage, a convolutional neural network (CNN) is integrated to perform TS recognition, enabling a complete detection–recognition pipeline. Experimental results show that YOLOv7-tiny achieves higher detection precision of 97%, while providing better speed and computational cost on resource-constrained devices, with Jetson Nano reaching 18.8 frames per second (FPS) and, on Jetson Xavier NX reaching 43 FPS. The integrated CNN model ensures reliable classification of detected TS with an accuracy of 99.54%. This work highlights the trade-offs between precision, speed, and power consumption and provides practical guidance for selecting detection and recognition architectures for embedded ADAS applications.
Parameters affecting protection coordination of high voltage distribution network with distributed energy resources Desai, Ishan; Chothani, Nilesh; Jacob, Paul
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10045

Abstract

Distributed energy resources (DERs) have become favorable sources of power against conventional power sources at distribution level because of their advantages like reduction in power loss and voltage drop, and environment friendliness. Integrating of DERs into high voltage distribution networks (HVDNs) introduces significant challenges to protection relay coordination because of their dynamic operational characteristics. This comprehensive study investigates key parameters influencing effective protection relay coordination in distribution networks (DNs), including fault current variability, DER penetration level, network topology, and relay settings. Through comprehensive simulation conducted on modified IEEE 4-bus HVDN with variable DER configurations through PSCAD/EMTDC 4.5 software, the research analysis evaluates the impact of these parameters on protection reliability and speed. Results indicate that high DER penetration leads to fault current increment by 10-20%, grid current contribution reduction by 10-15% as well as relay current setting alterations approximately by 30-40% which necessitates adaptive protection schemes. Network topology and relay coordination strategy significantly affect fault detection and isolation process. The finding provides understanding of parameters affecting protection coordination which helps in its optimization and having reliable and secure operation of DN.
Optimizing grouper catch efficiency using AI-controlled light fishing in Pamekasan Sampurno, Bambang; Sukma Adjie, Galih; Mashuri, Mashuri; Rusdiyana, Liza; Suhariyanto, Suhariyanto
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10697

Abstract

Light fishing techniques are widely used to improve marine fish catches by adjusting the spectrum and intensity of light-emitting diodes (LEDs) to attract specific species. Grouper (Epinephelus spp.) are known to respond well to red light, although the optimal light intensity remains unclear. This study proposes a dual system consisting of a pulse-width modulation (PWM)-based red LED brightness controller and an artificial intelligence (AI) fish detection module using the YOLOv4 algorithm. LED brightness was varied at duty cycles of 50, 100, 150, 200, and 250 (1 kHz), producing luminous flux values between 120 and 650 lm. Field experiments conducted at the Pamekasan coast using 10 grouper samples showed that 80% of fish aggregated at a duty cycle of 150 (~420 lm at 1.5 m). ANOVA results (p0.05) indicated significant differences among light intensities, with the highest attraction observed at this level. These findings suggest the existence of an optimal light intensity threshold. Further studies with larger samples and multi-site experiments are required to validate these results and evaluate ecological impacts.
Smart charging of electric vehicles at a charging station using machine learning and pressure pad energy harvesting Tadi, Kumara Swamy; Swapna, Ganapaneni; Rao, Kambhampati Venkata Govardhan; Kumar, Malligunta Kiran; Teja, Srungaram Ravi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10635

Abstract

The rapid growth of electric vehicles (EVs) demands intelligent, cost-effective, and sustainable charging solutions. This paper introduces a smart EV charging station system that integrates machine learning (ML) with pressure pad–based energy harvesting. The system forecasts energy demand, predicts vehicle types and slot needs, and recommends optimal charging times using real-time data such as state of charge (SoC), battery health, and user behavior patterns. ML models such as long short-term memory (LSTM) and random forest are employed to ensure accurate scheduling and forecasting. A smart display, the display slot indicator (DSI), powered by sensors and station data, guides users with live cost, time, and slot availability, including alternate suggestions during peak demand. The pressure pad not only contributes to energy recovery but also aids in real-time vehicle detection and traffic regulation within the station. With scalable capacity and intelligent automation, this system can support more than 400 EVs per day, minimizing operational load and energy waste while maximizing convenience and sustainability.
Improved no-line-of-sight static and dynamic sensor data classification using KNN algorithm with PLS model Jiya, Enoch Adama; B. Oluwafemi, Ilesanmi; Ibikunle, Francis Ayoleke; Nik Anwar, Nik Syahrim
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9437

Abstract

The rising cases of structural collapses across the world have aggravated the problem of finding people under rubble as part of search and rescue (SAR) effort. The conventional search techniques, namely drill operation and the use of dog searching, are usually slow, labour-intensive and unsuccessful in difficult debris setting. Radar systems, though non-invasive, are limited by attenuation and multipath interference in non-line-of-sight (NLOS) environments. The proposed research will contribute to the improvement of victim recognition by creating an advanced machine learning (ML) model that will operate in the most challenging environmental settings. It suggests a modular prediction model combining both the K-nearest neighbor (KNN) and partial least squares (PLS) to extract features and reduce the dimensions. The procedure includes the derivation of essential signal characteristics, dataset validation, PLS application to get limited and discriminative feature amounts, and KNN classification under conditions of both fixed and dynamic conditions. Experimental findings indicate classification scores of 87.87 and 75.70 respectively in case of static and dynamic data. These results validate the practicability of the suggested solution in enhancing the forecasting accuracy during NLOS circumstances and emphasize its possible use in enhancing quicker, more dependable, and evidence-based official choices during the actual SAR operations.
Forecasting graduate student enrollment in university using regression analysis Dwiansyah, Anggraini; Fahrurrozi, Imam; Fakhrurrifqi, Muhammad; Farooq, Umar; Alfian, Ganjar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9713

Abstract

The government ensures educational quality in universities through a quality assurance (QA) system implemented via accreditation, which evaluates both study programs and institutions. A key concern in accreditation is the decline in new student enrollment, making accurate predictions of enrollment numbers essential for quality assessment. This study proposes a linear regression (LR) model to forecast future university student enrollments based on enrollment figures from the previous year as input feature. Using a dataset from one of Indonesia’s leading university spanning 2013 to 2023, the experimental results demonstrate that the LR model outperforms other regression techniques, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF). The LR model achieves R² values between 0.87 and 0.95, reflecting a strong linear relationship between current and future student numbers. It also delivers high accuracy, with root mean square error (RMSE) values ranging from 11.72 to 41.21 per year. The trained LR model has been integrated into a web-based system, offering data visualization and enrollment predictions to support university management in monitoring quality, addressing enrollment challenges, and facilitating informed decision-making.
An efficient spectrum sensing method using convolutional neural network and filter banks for cognitive radio Ouamna, Hamza; El-Haryqy, Noureddine; Kharbouche, Anass; Madini, Zhour; Zouine, Younes
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10284

Abstract

The rapid advancement of connected vehicle technologies has intensified the need for efficient spectrum utilization. Cognitive radio (CR) enables dynamic access to underutilized spectrum, with spectrum sensing playing a key role in detecting primary users (PU). This study introduces a novel spectrum sensing approach that integrates filter bank (FB) signal decomposition with convolutional neural networks (CNNs)—referred to as filter bank decomposition and convolutional neural network (FB-CNN)—to enhance detection performance compared to conventional methods. Unlike traditional techniques such as energy detection (ED), the proposed FB-CNN leverages the frequency components extracted by the FB as CNN inputs, enabling robust identification of PU signals across multiple modulation schemes, including BPSK, QAM, FSK, and GMSK. Simulation results demonstrate substantial gains, particularly in low-SNR scenarios: for example, at an SNR of 5 dB, FB-CNN achieves a detection probability of 89% for 2-FSK, compared to only 22% with ED—representing a fourfold improvement. These findings highlight the novelty and effectiveness of FB-CNN in significantly improving spectrum sensing reliability for connected vehicle networks operating in challenging signal environments.
A new approach for sentiment and geopolitical stance analysis of Reddit comments on the Israel-Palestine conflict Upadhye, Gopal D.; Joshi, Hruishikesh; Rajput, Satpalsing Devising; Jadhav, Ranjana S.; Moralwar, Pranav; Muttyalwar, Tejas; Mehendale, Yash; Mane, Siddharth; Mishal, Shivam
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11132

Abstract

The paper focuses on the persistent conflict between Israel and Palestine. of world discussion, with such sites as Reddit containing a great deal medium for public expression. This study uses natural language processing (NLP) including valence aware dictionary and sentiment reasoner (VADER) for sentimental analysis and latent Dirichlet allocation (LDA) for topic modelling of geopolitical positions in comments of 400,000 posts belonging to the topics subreddits. A sentiment analysis using VADER shows that negative sentiments observed and indicate that such sentiments prevail throughout fear of aggression, human suffering, and threat. media biases. LDA analysis for topic modelling. The process of LDA reveals such important topics as humanitarian ones. media discourses, polemizing and appeals for calm. Furthermore, the study which divides comments into geopolitical attitudes – support for or yet, Israelis, supporters of Palestine, opponents of both, and neutrals, offering a contingency comprehensive conception of public perspectives. The results emphasize the effectiveness of large-scale. Of the big data pre-processing techniques, NLP can be used to understand geopolitics and its intricate dynamics. educate policy makers, researchers, and media planners.
Digital maturity assessment models in public administration: a systematic review Mendoza Dionicio, Miguel Abdias; Cano Lengua, Miguel Ángel; Rodríguez, Ciro
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9817

Abstract

Digital transformation (DT) is accelerating societal change and creating major challenges for public organizations seeking to improve efficiency through digital technologies. However, its measurement remains a conceptual and methodological challenge. This study presents a systematic literature review (SLR), conducted under the PRISMA protocol and PICOC strategy, focusing on digital maturity models applied to public administration (PA) between 2020 and 2024. The review covers both scientific databases and institutional gray literature. Five critical aspects were analyzed: included dimensions, internal structural relationships, empirical validation, predictive capacity, and contextual conditions of applicability. Results reveal a recurrent set of dimensions—technology, processes, data, people, and governance—yet with high heterogeneity in levels and approaches. Only a minority of models incorporate causal structures, and fewer than half have been empirically validated. Predictive capacity is almost absent, except for one Bayesian network model. Institutional factors such as digital leadership, budget, and regulatory frameworks strongly influence applicability. Unlike previous reviews, this study integrates a bibliometric analysis and a critical synthesis of enablers and barriers. It concludes that current models are useful for diagnosis but require improvements in structure, validation, and anticipation, providing an updated reference framework for researchers and policymakers in digital governance.

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