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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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Enhanced air quality index classification: leveraging genetic algorithm and SMOTE for accurate assessments in Indian cities Komal Kumar Napa; Ayodeji Olalekan Salau; Angati Kalyan Kumar; Sepiribo Lucky Braide; Aitizaz Ali; Ting Tin Tin
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.v15i4.9996

Abstract

The escalating air pollution levels in Indian metropolitan regions necessitate robust predictive systems for air quality assessment. This study presents an advanced air quality index (AQI) forecasting model leveraging the radial basis function (RBF) kernel-based extreme learning machine (ELM) optimized using a genetic algorithm (GA). The proposed model is evaluated on real-time AQI datasets from four major Indian cities: Vishakhapatnam, Delhi, Hyderabad, and Patna. Initial experiments without class balancing yielded prediction accuracies of 83.9%, 88.3%, 87.0%, and 86.9% respectively. To address the class imbalance and enhance predictive performance, the synthetic minority oversampling technique (SMOTE) was applied. Post-balancing, the model achieved significantly improved accuracies of 93.9%, 94.7%, 92.3%, and 96.2% across the respective cities. These results underscore the effectiveness of integrating SMOTE with RBF-ELM for AQI prediction and demonstrate the critical role of data balancing in improving model generalizability. The proposed approach offers a promising solution for urban air quality monitoring and can assist policymakers in formulating timely interventions to mitigate health risks associated with air pollution.
Robust optimization model for capacitated vehicle routing problem in waste transportation under demand uncertainty Hendra Cipta; Rina Widyasari; Raisha Zuhaira Dongoran
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.v15i4.11274

Abstract

Urban waste transportation systems often experience inefficiencies due to uncertainty in daily waste generation, leading to vehicle overloads, increased operational costs, and environmental impacts. This study proposes a robust optimization model for the capacitated vehicle routing problem (RO-CVRP) to explicitly address demand uncertainty in municipal waste collection. A budgeted uncertainty parameter gamma is incorporated to control the level of protection against worst-case deviations. Initial routes are generated using the Clarke-Wright savings (CWS) algorithm and subsequently evaluated within a robust optimization framework. Computational experiments are conducted using real data from temporary disposal sites (TPS) in Tanah Enam Ratus Subdistrict Medan City, with a vehicle capacity of 10 m³. The results show that higher gamma values produce more conservative routing solutions, increasing the number of vehicles while reducing the risk of capacity violations. Price of robustness (PoR) analysis highlights the trade-off between transportation cost and reliability, confirming the model’s effectiveness for resilient waste logistics planning.
Content categorization using enhanced convolutional neural network for library book availability prediction Geetha Jayabalan; Kavitha Venkatesh
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.v15i4.11364

Abstract

Deep learning (DL) techniques analyze textual data, but existing research has not explored heuristic approaches for extracting crucial text vectors. Essential preprocessing techniques for cleaning global and local text are also required. This study proposes a DL-based framework to predict book availability based on book-title. The proposed methodology has four phases: data preprocessing, text preprocessing, feature selection, and classification. Missing values, duplicates, and feature extraction are handled during data preprocessing to obtain clean data. Then, lemmatization and bidirectional encoder representations from transformers (BERT) vectorization are applied to derive feature vectors from each word in text preprocessing. Four feature selection algorithms-firefly algorithm (FA), artificial immune system (AIS), genetic algorithm (GA) and ant colony optimization (ACO) were used to select the crucial feature vectors. Comparatively, ACO obtained 90.87% accuracy, 90.87% precision, 100% recall, 91.94% F-score, and was selected as the optimal feature selection algorithm. The extracted features from ACO are trained using convolutional neural network (CNN) to predict book availability. Experimental results show that CNN achieved best performance than decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM) and other state-of-art methods, i.e., 99.07% accuracy, 99.83% precision, 99.24% recall, and 99.54% F-score for analyzing textual data.
Fractional Perona–Malik-based processing for noise reduction and structure preservation in red, green, blue Pap smear images Syaiful Anam; Normi Abdul Hadi; Avin Maulana; Indah Yanti; Suhaila Abd Halime
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.v15i4.11246

Abstract

Cervical cancer screening relies heavily on Pap smear analysis, yet image noise, low contrast, and overlapping cellular structures continue to limit diagnostic accuracy and the performance of automated systems. This study introduces a fractional Perona–Malik diffusion (FPMD) framework that extends the classical anisotropic diffusion model using fractional-order operators to achieve more flexible, edge-sensitive smoothing. The method is applied to red, green, blue (RGB) Pap smear images and benchmarked against classical PMD and conventional filters using entropy, blind/referenceless image spatial quality evaluator (BRISQUE), and edge preservation index (EPI). FPMD yields substantial improvements, achieving the lowest BRISQUE score (18.88) and the highest EPI values (>0.92) across all channels, indicating superior structural preservation and perceptual quality. While classical PMD produces slightly higher entropy, it introduces artifacts that degrade visual realism. FPMD provides a more controlled enhancement, producing diagnostically meaningful contrast and clearer cytological boundaries. These results highlight its potential as a robust preprocessing tool for both manual assessment and artificial intelligence (AI)-assisted cervical cancer screening.
Design and implementation of a mobile application for public transportation services in tourist destination area Sazilah Salam; Norazlina Shafie; Emaliana Kasmuri; Jack Febrian Rusdi
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.v15i4.8778

Abstract

Efficient public transportation is important for tourism cities like Melaka, Malaysia, where fragmented services and limited real-time information reduce user satisfaction and contribute to congestion. Existing transport applications often focus on a single mode and provide limited tourist-oriented functions. This study designed and implemented myGPT, a mobile application that integrates bus, taxi, and trishaw services into a unified platform with real-time information, cashless payment, and social interaction features. Using a design, development, and research (DDR) approach, user requirements were identified through a literature review and surveys, followed by system design, prototype development, and pilot testing. The myGPT framework includes modules for multimodal service discovery and trip planning, GPS-based nearby vehicle visibility, QR-code payment, and in-app communication and feedback. Pilot implementation results indicate improved trip coordination, usability, and user trust. The findings suggest that combining multimodal integration, real-time tracking, payments, and social features can support smarter tourist mobility and encourage sustainable urban transport in heritage cities.
A novel CAPTCHA mechanism: virtual keyboard for robust user authentication B Deone, Jyoti; Kundale, Jyoti; Bhagwat, Sumedha
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.v15i4.10147

Abstract

In the current digital age, the growing prevalence of cybersecurity threats has heightened the importance of securing online platforms. Completely automated public turing test to tell computers and humans apart (CAPTCHA) mechanisms are commonly employed to protect these platforms from spam, a prevalent type of online identity theft, and to strengthen defenses against automated attacks. This study introduces an innovative method to improve security against such automated threats. A key feature of the proposed system is the implementation of a virtual keyboard, which plays a central role in user authentication. The system utilizes a text recognition algorithm to authenticate users in real-time, effectively minimizing the risk of unauthorized access and ensuring enhanced security. Furthermore, the system is capable of detecting and managing imprecise user inputs, thereby increasing its reliability. Experimental findings highlight the system’s efficacy in mitigating comment spam on blogs, demonstrating its robustness against optical character recognition (OCR)-based attacks. Achieving an 87% success rate in user authentication, the system proves to be a strong defense against automated threats, reinforcing its overall security framework. This study underscores the potential of the proposed approach to advance cybersecurity measures for online platforms.
Design and realization of X-band wideband directional coupler using elliptical multilayer substrate architecture Shilpa Ankit Rana; Ashish Kalubhai Sarvaiya
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.v15i4.11047

Abstract

This paper presents the design analysis and realization of a wideband directional coupler tailored for X-band microwave applications. Directional couplers are critical components in modern microwave systems, where wide bandwidth and compact size are essential. The proposed design employs a multilayer configuration using TLY-5 substrate (epsilon r=2.20 and tangent delta=0.003), with a thickness of 0.508 mm and 17 µm copper cladding. The coupler is designed and simulated using Ansys high frequency structure simulator (HFSS) and subsequently fabricated for performance validation. It achieves a coupling factor of 3.5±0.6 dB across the 7–14 GHz range, with a transmission loss of 2.9±0.5 dB. Both return loss and isolation consistently exceed 20 dB throughout the operational band. The design exhibits a fractional bandwidth (FBW) of 66.67%, confirming its broadband capability. With a compact footprint of 2.475 lambda g ×1.485 lambda g, the coupler offers reasonable impedance matching and broadband performance, making it well-suited for integration in microwave circuits, particularly in variable attenuators and wideband communication systems.
Prediction of carbon emissions from vehicles using interpretable neural networks Ridho Sholehurrohman; Mochammad Reza Habibi; Joko Triloka
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.v15i4.12064

Abstract

Vehicle carbon dioxide emissions are an important environmental concern that requires prediction methods with both high accuracy and transparent explanations. This study proposes an interpretable neural network (INN) framework for estimating vehicle carbon dioxide emissions using a public Canadian fuel-consumption and emissions dataset. A feed-forward neural network is employed for regression, while SHapley additive exPlanations (SHAP) are used to interpret the contribution of each input feature. The proposed model is compared with linear regression, Bayesian regression, support vector regression (SVR), and extreme gradient boosting (XGBoost). Experimental results show that the neural-network model achieves a root mean squared error (RMSE) of 4.98, a mean absolute percentage error (MAPE) of 0.0128, and a coefficient of determination (R-squared) of 0.9926, outperforming all baseline models. SHAP analysis identifies combined fuel economy, city fuel consumption, and highway fuel consumption as the most influential predictors. Higher fuel economy is associated with lower predicted emissions, whereas higher city and highway fuel consumption increases predicted emissions. These findings demonstrate that the proposed framework provides accurate and interpretable vehicle carbon dioxide emission estimation.
Early detection of autism spectrum disorder through hybrid deep learning and classical machine learning approaches Khairina Ahmad Khair, Aina; Mohd Yaakob Wan Bejuri, Wan; Murtadha Mohamad, Mohd; Kadar, Masne; Kadim, Zulaikha; Sh-Hussain, Hadrina; Wahyuni, Deasy; Kasmin, Fauziah; Hea Choon, Ngo; Jaya Kumar, Yogan
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.v15i4.10905

Abstract

Early detection of autism spectrum disorder (ASD) is essential for timely intervention. This study presents a hybrid artificial intelligence framework for non-invasive ASD pre-screening using children’s coloring, drawing, and handwriting activities. The proposed framework combines deep convolutional neural networks (VGG16, ResNet50, and EfficientNetB0) as feature extractors with a support vector machine (SVM) classifier to distinguish four diagnostic categories: non-ASD, mild ASD, moderate ASD, and severe ASD. Experimental results demonstrate task-specific performance across architectures. ResNet50–SVM achieved perfect classification for coloring tasks, with 100% accuracy, precision, recall, and F1-score. VGG16–SVM performed best for drawing, achieving 88% accuracy and recall, 89% precision, and an F1-score of 87%. EfficientNetB0–SVM produced the highest handwriting performance, achieving 96% across all evaluation metrics. These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool. Future work will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.
Explainable classification of seizures and other patterns of harmful brain activity in critically ill patients Manikandan Arunachalam; Sanjay Thiyagarajan; Nagandla Chirudeep
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.v15i4.10181

Abstract

Accurate detection and classification of seizures from electroencephalography (EEG) signals play a vital role in enabling timely medical interventions and treatments for neurological disorders. Currently, EEG recordings are analyzed exclusively by trained human experts. Although essential, this manual process is time-intensive, costly, and prone to fatigue-related errors, in addition to inconsistencies between reviewers. In this work, we propose a deep neural network (DNN) model equipped with interpretable layers designed to classify seizures and other abnormal brain patterns, such as periodic discharges, rhythmic delta activity, and miscellaneous pathological events. The proposed DNN architecture incorporates explainable components that allow clinicians to trace and understand the model’s reasoning process, fostering confidence and aiding clinical decision-making. This combination of deep learning with interpretability mechanisms is novel and overcomes several limitations of traditional approaches. The method is validated using a publicly available EEG dataset, achieving state-of-the-art accuracy while maintaining interpretability that supports expert review. This study contributes to the growing field of EEG-based seizure detection by bridging the gap between high-performance DNN-based models and the need for clinical transparency. Unlike existing methods, our approach employs an ensemble of three specialized DNN, each capturing complementary feature representations. This ensemble framework improves both interpretability and robustness of the classification results.

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