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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 3,202 Documents
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Freshness detection of fruits and vegetables using multi-task convolutional neural network and ResNet-50 Sajeeb Kumar Ray; Anomik Kumar; Abu Saleh Md. Motassem Billah; Naima Islam; Md. Mynoddin; Md. Manik Ahmed; Md. Anwar Hossain
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11368

Abstract

Computer vision-based assessment of the freshness of fruits and vegetables is imperative for many agricultural applications, including automated harvesting and oversight of the supply chain. This manuscript investigates the advancement of deep convolutional neural networks (CNNs) aimed at detecting fruit freshness through the implementation of multi-task learning (MTL). The proposed models, MTL_ResNet-50 and MTL_CNN, leverage shared feature extraction to concurrently enhance freshness detection and fruit-type classification tasks. The MTL_ResNet-50 and MTL_CNN architectures attained accuracies of 99.32% and 97.95%, respectively. By exceeding the efficacy of established methodologies such as single-task learning and manual feature extraction techniques and by employing an imbalanced dataset to bolster the robustness of our models, we address significant gaps within the existing literature. Furthermore, we markedly surpassed the performance of prior approaches, such as InceptionV3 and CNN_bidirectional long short-term memory (CNN_BiLSTM), resulting in substantial implications for enhancing food quality and safety within both agricultural and consumer domains. These implications encompass the improvement of automated quality control mechanisms within the food industry, the mitigation of food waste, and the assurance of heightened standards for food safety. Future research initiatives may concentrate on augmenting model scalability, refining computational efficiency, and investigating additional applications within agricultural technology.
Detection of stages in diabetic retinopathy using computer aided ensemble network Rayudu Prasanti; Rajyalakshmi Uppada; Leela Kumari Balivada
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10407

Abstract

Diabetic retinopathy (DR) is one of the progressive micro vascular disorders of diabetes and a major cause of preventable blindness in the world. Manual ophthalmologist evaluation is costly in terms of time and more likely to have inter-observer error, whereas current automated methods tend to fail to differentiate between intermediate stages of DR; yet, proper assessment of DR severity is critical to its successful intervention. This paper suggests a framework of hybrid ensemble deep learning (DL) model that incorporates VGG16, InceptionV3 and ResNet50 based on a weighted feature fusion model and a feature-scaled parametric activation (FSPA) model to maximize inter-classes separability. The Kaggle EyePACS dataset containing 35,126 retinal fundus images was used. Image normalization, contrast enhancement, and data augmentation improved robustness to class imbalance. The overall accuracy of the proposed ensemble was 95.0% and the sensitivity and specificity were 92.0 and 94.0 respectively and the quadratic weighted Kappa (QWK) was 0.91, with up to +5% improvements in accuracy over individual convolutional neural network (CNN) baselines. Although there are still limitations to differentiating moderate versus severe DR, the findings demonstrate that the proposed framework enables consistent and reliable DR grading for large-scale clinical screening.
Improved chicken swarm optimization with RNNs for multi-label classification in imbalanced data M. Priyadharshini; Narendruni Lakshmi Priya; Anitha Vippdapu; Subrata Chowdhury; Thi-Thu Nguyen; Duc-Tan Tran; Duc-Nghia Tran
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11491

Abstract

Multi-label classification (MLC) seeks to make multiple predictions for an instance by identifying associations between labels, thereby enhancing prediction capability. The paper presents a new method for combining improved chicken swarm optimization (ICSO) feature selection with recurrent neural networks (RNNs) for MLC. ICSO overcomes feature selection issues and minimizes noise and redundancy in imbalanced datasets, whereas RNNs learn label dependencies to achieve higher accuracy. It is initially normalized using the Z-score method and then analyzed for dimensionality reduction using principal component analysis (PCA). Our approach is more accurate, more precise, better at recall, and achieves higher F-measure and specificity, and a lower error rate than current methods such as multi-label k-nearest neighbors (ML-KNN), fuzzy rough set learning with label-specific features (FRS-LIFT), and adaptive synthetic data for multi-label classification (ASD-MLC). The paper shows that ICSO is useful for augmenting RNN-based multi-label classification and may be applied in medical diagnosis and bioinformatics.
Speech-to-text model comparison using XLS-R, XLSR-53, and Wav2Vec 2.0 Juan Hebert; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11431

Abstract

Automatic speech recognition (ASR) systems have achieved significant progress in recent years; however, their performance remains limited for low-resource languages such as Indonesian. Multilingual ASR models are often expected to generalize across languages, yet they frequently underperform when applied to underrepresented languages without sufficient adaptation. This study presents a comparative evaluation of three ASR models—Wav2Vec 2.0, XLS-R, and XLSR-53—on Indonesian speech to analyze the impact of monolingual fine-tuning versus multilingual pretraining. The evaluation was conducted using approximately 28 hours of validated Indonesian speech from the Common Voice Corpus version 13. Model performance was assessed using word error rate (WER) without employing any external language model to ensure a fair comparison. Experimental results demonstrate that Wav2Vec 2.0, which is fine-tuned specifically for Indonesian, achieves substantially lower WER compared to the multilingual models. Qualitative analysis further confirms that multilingual models exhibit higher omission and substitution errors. These findings indicate that language-specific fine-tuning plays a more critical role than multilingual generalization in achieving accurate ASR for Indonesian. The results provide practical guidance for deploying ASR systems in low-resource language scenarios and highlight the importance of targeted model adaptation.
Robust stall detection of three-phase induction motors using transient-aware current-speed monitoring Muchlas Muchlas; Herman Dwi Surjono; Tole Sutikno; Barry Nur Setyanto
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.13216

Abstract

Reliable stall detection is essential for protecting three-phase induction motors from thermal damage while maintaining uninterrupted industrial operation. Conventional threshold-based protection methods often struggle to distinguish transient overload events from sustained stall conditions, resulting in nuisance trips or delayed fault detection under dynamic operating conditions. This paper proposes a robust transient-aware stall detection framework that combines stator current monitoring, rotor speed monitoring, startup inhibition, adaptive activation logic, and hold-time verification within a lightweight threshold-based decision process. The proposed algorithm was implemented and evaluated using a MATLAB/Simulink model of a three-phase squirrel-cage induction motor under representative operating scenarios, including motor startup, temporary overload, and sustained stall conditions. Simulation results demonstrate that the proposed framework successfully suppresses unnecessary trips during short-duration overloads by allowing the motor to re-accelerate after transient disturbances. Conversely, when abnormal operating conditions persist beyond the predefined hold time, the algorithm reliably identifies an actual stall and activates the protection relay to disconnect the motor. By integrating temporal verification with simultaneous current and speed monitoring, the proposed framework effectively discriminates recoverable transient events from sustained stall conditions while maintaining low computational complexity. These characteristics make the proposed method suitable for practical real-time induction motor protection in industrial automation applications.
HybridArrhyNet: a CNN-BiLSTM framework with attention mechanism for robust arrhythmia classification from ECG signals Shanavaz Mohammed; Mohd Miskeen Ali; Muneeruddin Mohammed; Kandi Trishaank
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10123

Abstract

Arrhythmia classification is critical for the early diagnosis of cardiac disorders that may lead to sudden cardiac arrest, stroke, or heart failure. However, accurate detection from electrocardiogram (ECG) signals remains challenging due to noise, class imbalance, and the need to identify diagnostically important signal segments. This study presents HybridArrhyNet, a deep learning framework that integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and an additive attention mechanism for robust arrhythmia classification. The CNN extracts spatial features from ECG signals, while the BiLSTM captures temporal dependencies in both forward and backward directions. The attention mechanism selectively emphasizes clinically relevant ECG segments, improving classification performance and interpretability. To address class imbalance, random oversampling and weighted cross-entropy loss are employed. The proposed model was evaluated on the MIT-BIH Arrhythmia Database containing over 100,000 annotated beats across five arrhythmia classes (N, S, V, F, and Q). Experimental results demonstrate superior performance, achieving 98.7% accuracy, 98.5% precision, 98.6% recall, and 98.5% F1-score. Ablation studies further confirm the effectiveness of each component, highlighting HybridArrhyNet as a reliable framework for automated arrhythmia detection.
Adaptive pulse impressed current cathodic protection using fuzzy logic control Raditya Burhani Assidqi; Mohd Rafi Adzman; Shaiful Rizam Shamsudin; Karisma Trinanda Putra; Aiman Arif Mohd Sollehuddin
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11818

Abstract

Corrosion in buried metal infrastructure poses significant economic and safety risks, requiring advancements over traditional impressed current cathodic protection (ICCP). This study presents an innovative pulse impressed current cathodic protection (PICCP) system integrating high- frequency pulse feeding with fuzzy logic control (FLC) for enhanced stability and energy efficiency. Utilizing an Arduino Uno and ESP8266 for real-time IoT monitoring via Blynk, the system was tested for 14 days in humus soil (10–20 kilo-ohm·cm, pH 5–6). Results demonstrate that PICCP consistently maintained off-potentials within the safe range of –1100 to –850 mV, whereas conventional ICCP failed by the eighth day, falling below – 750 mV. The adaptive PICCP system, operating between 44–52 kHz, achieved a tenfold improvement in energy efficiency, requiring only 0.74– 0.80 mA compared to 9–10 mA for ICCP. These findings validate the PICCP framework as a robust, scalable solution for embedded metallic infrastructures, effectively combining automated remote monitoring with adaptive electrochemical protection.
CLEAR: congestion-level estimation and adaptive routing for Indian urban traffic Sreejith Karunakaran; Senthilkumar Mathi; Preeja Pradeep
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10239

Abstract

Urban traffic congestion in India poses significant economic and environmental challenges, necessitating early detection for effective management. This study presents a novel approach to real-time congestion prediction, addressing the need for a mechanism that can adapt to India's diverse traffic patterns while minimizing computational complexity. Unlike previous studies that focused on long-term forecasts, this research proposes a lightweight, resource-efficient model capable of predicting congestion levels at 15-minute intervals. The model utilizes a real-time dataset collected from the Palakkad region in southern India. To improve prediction accuracy, the method employs ensemble methods. Experiments have shown that the random forest algorithm is 98.6% accurate at predicting traffic jams, which is better than previous research in this area. Additionally, the study develops a best-route recommendation system based on the experimental results. This research contributes to the field by offering a practical approach to mitigating urban traffic congestion in India, with applications in other cities characterized by diverse traffic patterns. The proposed model's ability to provide accurate short-term predictions while maintaining computational efficiency represents a significant advancement in traffic management strategies for developing urban ecosystems. Furthermore, the study underscores the efficacy of data-driven decision support systems in optimizing urban mobility and reducing vehicular emissions.
Dynamic alpha factor optimization for hybrid semantic similarity in french word sense disambiguation Btissam El Janati; Adil Enaanai; Fadoua Ghanimi
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11680

Abstract

Word sense disambiguation (WSD) remains critical for French, where polysemy complicates semantic interpretation. Hybrid approaches combining lexical and semantic methods typically rely on static weighting parameters that fail to adapt to varying contexts. A hybrid architecture integrating fuzzy Jaccard similarity with sentence-bidirectional encoder representations from transformers (SBERT) embeddings is proposed. A machine learning-based dynamic weighting mechanism replaces the fixed alpha=0.7. A Ridge regression model predicts the optimal alpha based on five features: entropy, Jaccard-SBERT disagreement, gloss length, context richness, and SBERT confidence. The model was trained on 20 sentences and validated on 13 sentences from a dataset of 33 ambiguous French phrases. Dynamic alpha achieves a 7.6% reduction in mean absolute error (MAE) (0.2397 to 0.2216) and a 7.3% reduction in root mean square error (RMSE) (0.2475 to 0.2294) compared to fixed alpha=0.7. Per-sentence gains reach 10.0%. Statistical analysis confirms significance (Wilcoxon, p=0.00195) with a small to medium effect size (Cohen's d=0.300). Feature coefficients reveal that disagreement (+0.41) and entropy (+0.32) are the most influential predictors.
A new compact printed ring bandpass filter structure for ISM band applications Soufiane El Maimouni; Fouad Aytouna; Jamal Zbitou; Stephane Ginestar; Mohammed El Gibari
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11426

Abstract

We introduce a novel, compact bandpass filter (BPF) that employs Ring coupling through a microstrip design. This filter features a ring resonator that mainly relies on capacitive coupling to achieve its resonant characteristic. The results from experimental fabrication confirmed the expected performance of the filter, showing strong agreement between the simulated and measured S-parameters. The filter is built on a RO4003C substrate that is 1.52 mm thick, with a dielectric constant of 3.38 and a loss tangent of 0.0027. Designed for a center frequency of 5.72 GHz and a bandwidth of 930 MHz, it exhibits low insertion loss and effective signal rejection, as supported by advanced electromagnetic simulations. This flexible and efficient filter design represents a significant step forward, offering great potential for innovative communication systems and wireless power transmission, especially when integrated with 5.8 GHz straight-through antenna arrays for wireless energy harvesting.

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