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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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Differential quality game with fuzzy information for assessing financial resources for air quality monitoring in cities Arkadii Chikrii; Volodimir Malyukov; Valery Lakhno; Inna Malyukova; Adlet Kassymbekov; Raissa Uskenbayeva; Gabit Shuitenov; Bauyrzhan Tynymbayev
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.v15i3.10545

Abstract

Rapid urbanization intensifies pressure on city air quality, making costeffective monitoring a governance priority. Existing sensor-placement approaches optimize coverage but often ignore strategic behavior of polluters and budget uncertainty, leading to fragile deployments. We propose a decision model that allocates monitoring funds via a bilinear differential game with fuzzy information between an environmental defender and a polluter. Unlike linear differential games solvable via the Cauchy formula, bilinear dynamics and non-measurable adversary strategies require a novel discreteapproximation method within a positional game scheme. The model captures dynamic financial interactions through membership functions and yields analytical characterizations of the defender's preference set and optimal pure strategies. Computational experiments on realistic scenarios illustrate stable funding regimes and support actionable guidance for urban planners: how much to invest, when, and where to expand monitoring stations to achieve resilient oversight under uncertainty. The framework can be embedded in intelligent decision-support tools for smart-city environmental management.
Osprey optimization algorithm for VGG16 hyperparameter optimization in breast cancer detection Urundai Meeran, Sabura Banu; Abdul Munaf, Nafeena; Velu, Vengadeshwaran
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.v15i3.11181

Abstract

Globally, breast cancer is one of the reason for mortality among women and accurate automated diagnosis remains a critical research challenge. This research is used to improve breast cancer classification performance by optimizing deep learning (DL) model hyperparameters using a bio-inspired optimization technique. The osprey optimization algorithm (OOA) is applied to fine-tune the hyperparameters of the VGG16 convolutional neural network (CNN) for histopathological breast cancer image classification. The optimized model is evaluated using a curated dataset and compared with established DL architectures, including AlexNet, Xception, InceptionV3, and ResNet50. Performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, specificity, AUC-ROC, Matthews correlation coefficient (MCC), log loss, and inference time. Experimental results indicate that the OOA-optimized VGG16 model achieves superior performance, with an accuracy of 97.7%, precision of 96.71%, recall of 97.79%, AUC-ROC of 99.92%, and MCC of 0.9449, while maintaining competitive computational efficiency. The results demonstrate that bio-inspired hyperparameter optimization significantly enhances classification reliability and diagnostic accuracy. In summary, integrating OOA optimization with the VGG16 architecture yields a dependable framework for breast cancer identification, making it a promising candidate for deployment in automated diagnostic support systems.
Design and realization of a fuzzy logic-based MPPT controller for PV systems using microcontroller Thakre, Mohan P.; Kumar, Badal; Kumar, Alok; Nilesh Thakur, Supriya; Kanekar, Krupali; M. Thakre, Pranali; K. Magadum, Prashant
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.v15i3.11394

Abstract

This study presents a microcontroller-based fuzzy logic control method for maximum power point tracking (MPPT) in photovoltaic systems under varying temperature and solar irradiation. The proposed controller is implemented on an 8-bit microcontroller and regulates the duty cycle of a pulse-width-modulation-driven DC-DC converter to extract maximum power from the photovoltaic array. Unlike conventional MPPT methods, the fuzzy logic approach provides faster response, improved flexibility, and stronger robustness against nonlinear current-voltage characteristics and converter switching effects. The system includes a photovoltaic array, sensing circuits, a DC-DC converter, and an embedded controller programmed with optimized C code for real-time operation. Experimental results show that the proposed method reaches the maximum power point quickly and maintains stable performance during environmental changes. It also improves energy conversion efficiency compared with traditional algorithms. Its low-cost hardware and simple embedded implementation make it suitable for practical photovoltaic applications and sustainable energy generation in renewable energy systems.
Toward optimal bankruptcy prediction: evaluating ensemble methods using Taiwan and U.S. financial bankruptcy data Idris, Nur Farahaina; Ismail, Mohd Arfian
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.v15i3.11159

Abstract

Bankruptcy filings have increased significantly in many countries, causing widespread concern across society and triggering various economic issues. One contributing factor to the global rise in corporate bankruptcies is the unstable nature of companies’ growth. This issue often driven by unclear financial strategies and weak business direction. Thus, bankruptcy prediction plays a vital role, enabling earlier intervention and allowing business owners to improve their financial strategies proactively. This research investigates the effectiveness of ensemble machine learning (ML) methods using random forest (RF), stacking, and adaptive boosting (AdaBoost) for the prediction of corporate bankruptcy using datasets from Taiwan and the U.S. In the experimental phase, the performance is assessed using accuracy, precision, recall, F1 score, relative absolute error, and time. RF scored the highest accuracy in the classification of Taiwan’s bankruptcy data with 97.067%, meanwhile, AdaBoost M1 obtained the highest accuracy in the classification of the U.S.’s bankruptcy data with 94.0075%. The research shows that these methods, particularly AdaBoost M1, can improve early-warning systems and provide actionable insights for financial risk management. The main contribution of this research is its cross-country comparison of ensemble methods for bankruptcy prediction.
Rapid and reliable approach for detecting milk spoilage Jamewar, Srushti; Patki, Ritika; Gharote, Nishad; Mathur, Tanika; Korde, Mridula
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.v15i3.11094

Abstract

Milk spoilage poses a major challenge to food safety, public health, and sustainability, often resulting in unnecessary waste across households and dairy supply chains. Traditional detection methods, such as smelling or boiling, are subjective, delay early identification, and frequently lead to misjudgment. This study proposes a machine learning (ML)–based spoilage detection framework that integrates real-time pH and carbon monoxide (CO) sensor data to classify milk as fresh, not fresh, or spoiled. multiple supervised learning models, including random forest, eXtreme gradient boosting (XGBoost), support vector machine (SVM), and decision tree, were trained and evaluated using datasets collected from raw and boiled milk samples under varying conditions. Performance was assessed using coefficient of determination (R²), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics to identify the most reliable model for shelf-life prediction. Experimental results show that random forest and XGBoost outperform traditional threshold-based approaches, with random forest demonstrating superior consistency and operational efficiency. The findings highlight the potential of intelligent, low-cost sensor–ML systems to significantly enhance early spoilage detection, strengthen food safety, and reduce milk wastage across domestic and industrial environments.
SignVerse: bridging communication through a bi-directional sign language translation system Upadhye, Gopal Dadarao; Wankhade, Shalini; Tukaram, Umbare Rupali; Kakade, Ankita; Agarwal, Mayur; Shinde, Dhanshree; Mahale, Manesh; Maindargi, Nujaim
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.v15i3.11141

Abstract

This study introduces SignVerse, a novel bi-directional sign language translation (SLT) system, to enhance communication between the hearing-impaired community and the general public. SignVerse makes real-time, two-way conversations easy for Indian Sign Language (ISL) users—no special hardware needed. The system uses smart artificial intelligence (AI) tech: computer vision, deep learning, and natural language processing (NLP). When someone types or speaks, the text/speech-to-sign module runs the input through NLP-based syntactic reordering and shows the ISL translation using a lively 3D avatar. On the flip side, the sign-to-text/speech module leverages MediaPipe to spot hand landmarks in real time, and the convolutional neural network-long short-term memory (CNN-LSTM) model accurately recognizes each gesture. Everything works together to help ISL users connect smoothly with others 94.8% recognition accuracy, less than 1.8-second translation latency, and more than 90% gesture clarity in user studies are all demonstrated by experimental evaluations. The lightweight model, which is optimized through knowledge distillation, guarantees excellent performance even on common consumer devices. With significant potential for societal impact, SignVerse is a significant step toward real-time, AI-driven ISL translation. When everything is taken into account, it is a dependable, scalable, and reasonably priced choice for inclusive communication.
Comparative study of CNN and fused 2D CNN-LSTM with CWT and STFT for power quality disturbance classification Bouchra Feriel Khaldi; Fatma Zohra Dekhandji; Abdelmadjid Recioui
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.v15i3.10719

Abstract

The integration of solar and wind energy has increased electricity generation but also introduced power quality disturbances (PQDs) that threaten grid stability. This study examines the detection and classification of five PQD types—voltage sag, swell, interruption, harmonics, and normal conditions—across noisy environments (0, 10, 20, and 30 dB) signal-to-noise ratio (SNR). Traditional methods— support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and 1D convolutional neural networks (1D CNN)—are evaluated on raw signal data, while advanced models—2D CNN and fused 2D CNN-LSTM—utilize time-frequency representations (continuous wavelet transform (CWT) and short-time Fourier transform (STFT)). Results show that deep learning (DL) models achieve high accuracy even in noisy environments, with the fused 2D CNNLSTM using CWT outperforming all other methods. Noise adversely affects feature extraction, with CWT consistently outperforming STFT under low SNR conditions. These findings demonstrate that combining DL models with robust time-frequency analysis and temporal modeling enhances PQD classification and supports dependable monitoring in smart grid environments.
Profiling student performance for multi-agent personalization in virtual reality Alaoui, Ghalia Mdaghri; Khabbachi, Ilhame; Zouhair, Abdelhamid; En-Naimi, El Mokhtar
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.v15i3.11118

Abstract

This study uses the open university learning analytics dataset (OULAD) to cluster student performance data to improve personalized learning. Three main aspects are the focus of the analysis: instructional involvement, behavior, and demographics. To create significant, comprehensible student profiles, the clustering algorithms k-means, k-modes, and k-prototypes were used for each dimension independently. In order to forecast student categories from input features, supervised classification models, such as support vector machines (SVMs) and random forests, were trained using these profiles as targets. Accuracy, F1-score, and cross-validation were used to assess the categorization models' performance. The outcomes demonstrate how well unsupervised and supervised learning strategies may be combined for adaptive learning. These profiles serve as a foundation for the future design of a multi-agent virtual reality (VR)-learning environment. In this envisioned system, specialized agents would handle behavioral adaptation, demographic personalization, and pedagogical coordination, offering a personalized learning experience tailored to each learner’s profile.
Performance analysis of classification models to determine the health status of edge computing devices Yauri, Ricardo; Palomino, Nora Bertha La Serna
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.v15i3.11905

Abstract

Artificial intelligence (AI) has contributed to the development of autonomous systems in the healthcare field by integrating machine learning models, whose evaluation on resource-limited hardware devices is important to ensure their efficiency. This research evaluates the performance of classification models in edge computing (EC) systems, considering metrics such as accuracy, latency, memory consumption, and energy efficiency on low-power microcontrollers using TinyML techniques. The processes involved include the development, implementation, and testing of algorithms on embedded hardware using differentiated preprocessing techniques and the validation of hypotheses through statistical analysis. The results show that the decision tree (DT) model is more efficient in terms of prediction time and energy consumption, while random forests (RFs) stand out for their greater accuracy. Furthermore, memory analysis reveals that models based on fully connected neural networks are more efficient in terms of RAM usage. This provides guidelines for selecting algorithms in resource-constrained environments.

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