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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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An RBF neural network–based MPPT with sliding mode and fuzzy control for PV systems using buck converter Le, Anh Van; Pham, Minh Van; Vu, Linh Thi To
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.v15i3.11797

Abstract

This paper proposes an integrated control strategy for maximum power point tracking (MPPT) in photovoltaic (PV) systems using a buck converter. The controller combines a radial basis function (RBF) neural network for uncertainty approximation, sliding mode control (SMC) for robustness, and fuzzy logic for adaptive tuning of the switching gain to reduce chattering. The complete RBF–SMC–fuzzy control law is derived, and closed-loop stability is proven using Lyapunov theory. Simulation results in MATLAB/Simulink under both resistive and battery charging loads show that the proposed method achieves fast tracking with a settling time of about 20 ms, a tracking efficiency higher than 99%, and a voltage ripple of approximately 1.2%. Compared with conventional methods, the proposed controller significantly reduces chattering and improves power extraction performance under irradiance and load variations.
Allergen detection based on food packaged products for eczema patients using optical character recognition method Rini, Dian Palupi; Mardilah, Riska Tri; Rizqie, Muhammad Qurhanul; Indah, Dwi Rosa; Utami, Alvi Syahrini; Morgan, Jovanic
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.v15i3.9739

Abstract

Eczema, also known as dermatitis, is a chronic skin condition that causes recurring episodes of dry and itchy skin. It can be managed through medication and by avoiding triggers like stress and certain foods. To help patients avoid food-related triggers, researchers conducted a study to detect allergenic food compositions in packaged products using optical character recognition (OCR) techniques, specifically open computer vision (OpenCV) and Tesseract. The study involved analyzing 120 images of food labels. The process included several steps: preprocessing the images by converting them to a text-friendly format (gray scaling, denoising, and thresholding), using Tesseract for text detection, followed by case folding and tokenization. The results showed that the system achieved an average text detection accuracy of 61.88% and an average allergen detection accuracy of 83.06%. The highest accuracy for text detection was 78.52%, and the highest accuracy for allergen detection was 100%. These findings suggest that OCR techniques can be a useful tool for helping eczema patients manage their diet and minimize flare-ups.
Fast and accurate cheat detection using deep learning algorithms Khabbachi, Ilhame; Mdaghri Alaoui, Ghalia; Zouhair, Abdelhamid
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.v15i3.11207

Abstract

The rapid expansion of online education, accelerated by the global health crisis of 2020, has introduced significant challenges in maintaining academic integrity due to the absence of physical supervision during remote examinations. As digital learning becomes a permanent component of modern education, ensuring fairness and credibility in online assessments has become a critical concern for educational institutions. This study proposes an intelligent deep learning (DL)–based framework for detecting non-compliant behaviors during online examinations using standard webcam video streams. The proposed system integrates real-time video monitoring with automated behavioral analysis by combining object detection and classification models. In particular, you only look once version 5 (YOLOv5) is employed for efficient facial and object detection, while a convolutional neural network (CNN) is used to classify cheating and non-cheating behaviors from extracted visual features. Experimental results demonstrate that the integrated YOLOv5–CNN architecture achieves high detection accuracy and low inference latency, making it suitable for real-time and scalable deployment in online proctoring systems. By enabling objective and automated monitoring, the proposed framework contributes to strengthening fairness, transparency, and trust in digital assessment environments, thereby supporting the long-term sustainability of online education.
Hybrid deep learning ensemble with score-based feature optimization for cyber attack detection in IoT systems Manoranjini, John; Gaddam, Venugopal; Raghavender, Kotla Venkata; Battu, Hanumantha Rao; Sunitha, Pamarthi; Shanmugam, Sathish Kumar
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.v15i3.11674

Abstract

The rapid growth of internet of things (IoT) devices have improved connectivity but also exposed networks to cyber threats. This study proposes a prediction-scoring-based ensemble deep learning model with prediction-scoring-optimized feature selection (EDLM-PSOFS) for intrusion detection in IoT systems. The model integrates random forest (RF) feature extraction with ant lion optimization (ALO)-tuned convolutional neural networks (CNNs) to balance accuracy and computational efficiency. Using the KDD Cup ’99 dataset containing 4.9 million traffic records and 41 features, the framework achieved 97% accuracy, 0.99 precision, and 0.97 recall within five epochs. Comparative evaluation shows faster convergence and reduced complexity than gated recurrent units (GRU), long short-term memory (LSTM), and support vector machine (SVM) baselines, demonstrating suitability for real-time, resource-constrained IoT deployments.
Analysis of arrhythmia detection and classification using electrocardiogram signals with decision tree algorithm Marpaung, Putri Juniarti; Anggreni Matondang, Nia; Margaretta Siregar, Rince; Ester Novelia Siahaan, Angelica; Dharma, Abdi; Turnip, Arjon; Turnip, Mardi
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.v15i3.10317

Abstract

Heart disease remains the primary cause of death globally, with arrhythmia diagnosis often limited by restricted access to medical personnel and the complexity of electrocardiogram (ECG) interpretation. Accurate arrhythmia classification is essential to prevent cardiovascular complications. The proposed method successfully categorized classify ECG signals into five categories: normal, abnormal, potentially arrhythmia, moderate arrhythmia risk, and highly potentially arrhythmia. Data were collected from 30 subjects under three activity scenarios: sitting, walking, and running. The proposed model achieved an accuracy of 99.4%, demonstrating strong potential for real-time monitoring applications. Performance evaluation was conducted using accuracy, precision, recall, and F1-score for each class. Although the dataset size remains relatively small, the findings highlight the effectiveness of decision tree as an efficient and interpretable classification method. Future research will involve validation using large-scale public databases like the arrhythmia database at MIT-BIH and comparisons with advanced methods including convolutional neural network (CNN), transformer-based models, and explainable artificial intelligent (XAI) frameworks.
Cocoon quality assessment and silk yield estimation YOLOv8 and EfficientNet-B0 Shivananda Shivanna; Lincy Meera Mathews; Omraj Ravindra Bhalke; Aryan Vats; Krrish Agrawal; Aditya Singh
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.v15i3.11190

Abstract

Silk production depends heavily on accurate cocoon grading, yet manual inspection is slow, inconsistent, and varies between operators. This creates problems in quality control and affects the final yield of raw silk. To address this, we present an automated system that uses computer vision to detect, separate, and grade silk cocoons without human involvement. The system combines a you only look once version 8 (YOLOv8) model for segmenting individual cocoons from tray images and an EfficientNetB0 classifier for identifying defects across six categories, including one qualified class and five defect types. After detection and grading, the pipeline also estimates the percentage of good cocoons and predicts silk yield based on standard industry measures. The model was trained on 3,068 cocoon samples and achieved 96.1% mean average precision (mAP) for segmentation and 97% accuracy for classification. The system can count cocoons, assess quality distribution, and provide batch-level yield estimates. This automated approach improves reliability, reduces manual effort, and offers consistent grading suitable for both farm-level and industrial environments. With low operating cost and simple deployment, the system supports modern, scalable, and data-driven sericulture.
Sustainable greenhouse using IoT and machine learning to optimize the microclimate for lettuce cultivation Rudy Ivan Jamjachi Yauri; Jorge Raul Herbozo Ramirez; Christian Ovalle
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.v15i3.9877

Abstract

Sustainable agriculture faces increasing challenges due to climate variability, which affects crop productivity and resource efficiency. This study proposes a sustainable greenhouse system that integrates internet of things (IoT) sensors and machine learning models to optimize the microclimate for lettuce cultivation. Environmental data, including temperature, humidity, and light intensity, were collected through IoT sensors and processed using machine learning algorithms, specifically neural networks and support vector machines (SVM), implemented on the Orange data mining platform. The results indicate that the neural network model achieved superior performance, reaching an accuracy of 99.99% in predicting optimal greenhouse climate conditions, outperforming the SVM model. The best-performing model was subsequently implemented on an Arduino-based IoT system to automatically regulate greenhouse conditions. The proposed system improved resource efficiency and supported optimal lettuce growth while promoting sustainable agricultural practices. These findings demonstrate that integrating IoT and machine learning can enhance greenhouse management, contributing to climate-resilient agriculture and improved food production systems.
Food traceability model using blockchain technology Tjahyadi, Rudy; Meyliana, Meyliana; Warnars, Harco Leslie Spits; Wiputra, Richard
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.v15i3.10703

Abstract

The complexity and globalisation of food supply chains raises concerns over food safety, authenticity and provenance. Data fragmentation, fraud and lack of transparency mean traditional traceability solutions are often ineffective and diminish consumer confidence. Blockchain technology can solve these limitations as it is decentralized, transparent and immutable. This study shows that blockchain technology has the potential to change the food traceability landscape by allowing food products to be securely and verifiably traced from raw ingredients to consumers. We discuss how such a system can improve openness, immutability, and information integrity. We will discuss the issues of scalability, interoperability, data security and stakeholder collaboration in the food sector in the implementation and adoption of blockchain. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) and design science research (DSR) methods were used to develop a blockchain-based food traceability model, ChickenTrax, from 33 papers from 2020 to 2025. Its goal is to enable real-world deployment within the poultry sector and to improve the knowledge of the advantages and limitations of the use of the blockchain technology for a more robust and reliable food traceability system (FSTS).
A BERT-based modular framework for automated English essay scoring via trait analysis Jasman Pardede; Rizka Milandga Milenio; Thalita Zharifa Nathania
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.v15i3.11235

Abstract

Automated essay scoring (AES) systems are commonly implemented using holistic scoring, which limits interpretability and prevents assessment at the writing trait level. As a result, such systems provide limited diagnostic and actionable feedback. To address this limitation, this study proposes a modular trait-based AES framework that separates structure and grammar evaluation while maintaining an integrated scoring mechanism. The proposed framework consists of two modules. The structure module evaluates the ideas, organization, and style traits using a bidirectional encoder representations from transformer-bidirectional long short-term memory (BERT-BiLSTM-Attention) architecture trained on the automated student assessment prize (ASAP) dataset. The grammar module evaluates the Conventions trait by applying a BERT-based grammatical acceptability classifier trained on the Corpus of linguistic acceptability (CoLA) dataset, followed by multinomial logistic regression to convert grammatical patterns into interpretable grammar scores. Experiments were conducted on the ASAP dataset and evaluated using the quadratic weighted Kappa (QWK) metric. The structure module achieved a QWK score of 0.7906 on the test set, while the grammar module obtained a QWK of 0.3923. The integrated holistic score reached a QWK of 0.7847. These results demonstrate that the proposed modular framework improves interpretability and scoring performance, supporting more objective and actionable essay evaluation for formative assessment in English language education.
Multi domain, multi-scale diagnostic modeling for histopathological breast cancer classifications Komal S. Gandle; Kshirsagar Dhananjay Bhanudas
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.v15i3.11152

Abstract

Breast cancer detection through histopathological imaging remains challenging due to complex tissue morphology, observer variability, and subtle differences between invasive and pre-invasive lesions. Conventional computer-aided diagnostic systems often rely on single-domain feature extraction, restricting multi-scale representation and clinical interpretability. To overcome these limitations, we propose a verified diagnostic framework integrating five analytical components for efficient and explainable breast cancer classification. The adaptive multi-level histopathological feature selection using cross-domain mutual information maximization (AMFSCDMIM) extracts highly informative morphological and frequency features with minimal redundancy. The deep hierarchical hybrid morphological– frequency encoding network (DH-HMFEN) refines spatial–spectral representations, while the multi-scale morphological attention classification network (MS-MACNet) applies adaptive attention across tissue structures for improved discrimination. The adaptive ensemble validation for breast cancer classification (AEV-BCC) calibrates confidence levels for enhanced reliability, and the comparative analytical performance validation with interpretability integrated metrics (CAPV-IIM) quantitatively evaluates model explainability using expert annotations. Experimental results on benchmark datasets achieve 96% accuracy, 0.98 area under the receiver operating characteristic curve (AUROC), and a 0.88 interpretability alignment score, outperforming existing methods. The proposed confidence-calibrated, multi-domain, and multi-scale framework enhances diagnostic precision and clinical trust in histopathology-based breast cancer detection.

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