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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 2,901 Documents
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Development of water quality monitoring system for fish farming B. Papolonias, Juffil; Q. Lavilles, Rabby; I. Miano, Joel
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7673

Abstract

Tilapia fish farming faces growing challenges from climate variability, environmental degradation, and the urgent demand for sustainable food production. However, traditional water quality monitoring methods remain manual and reactive, often resulting in compromised fish health and reduced farm productivity. Addressing this need, this study designed and developed a water quality monitoring system utilizing the internet of things (IoT) and embedded systems to enable real-time, proactive management. Guided by the software development life cycle (SDLC), the methodology focused on planning and analysis, system design and development, and testing and evaluation. The system integrates key water quality sensors, including pH, temperature, dissolved oxygen (DO), and electrical conductivity (EC), identified as critical parameters affecting tilapia health. These sensors were interfaced with Arduino Nano and ESP32 Dev Kit microcontrollers, forming the sensing layer of the system. Sensor data were transmitted to the ThingSpeak IoT platform for real-time visualization and storage. Validation results revealed a low mean absolute percentage error (MAPE), indicating an acceptable sensor performance. User evaluation, based on the technology acceptance model (TAM), indicated that the system was perceived as useful, user-friendly, and valuable for aquaculture management. Overall, the system enables real-time water quality monitoring, supporting a more responsive and sustainable environment for tilapia fish farming.
Hybrid CNN-ViT integration into Siamese networks for robust iris biometric verification Latif, Samihah Abdul; Sidek, Khairul Azami; Hashim, Aisha Hassan Abdalla
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9182

Abstract

Iris recognition has emerged as a critical biometric verification method, valued for its high accuracy and resistance to forgery. However, traditional convolutional neural network (CNN)-based models, despite their strength in extracting local iris features, struggle to capture global dependencies, which limits their generalization across different datasets. Additionally, conventional classification-based approaches struggle to accurately verify new individuals with limited training data. Thus, this study proposed a hybrid CNN-vision transformer (CNN-ViT) model within a Siamese network to enhance one-shot learning capability by combining CNN’s local feature extraction with vision transformers (ViT’s) global attention. To evaluate its performance, the hybrid model was compared with VGG16 and ResNet under the same training conditions for 20 epochs. VGG16 and ResNet rely on pre-trained models, whereas the hybrid CNN-ViT model is specifically designed to achieve this task with an increment to 98.9% training accuracy, surpassing the TinySiamese model's benchmark accuracy. It also attained a recall of 75%, demonstrating strong sensitivity in correctly identifying positive matches. The hybrid model maintained an excellent balance between learning and generalization by employing the binary cross entropy (BCE) loss function. These findings contribute to the development of efficient iris recognition systems, paving the way for advanced biometric applications in financial transactions, border control and mobile security.
Prediction of stock market price for investors using machine learning approach Ayokunle Esan, Omobayo; Oladayo Esan, Dorcas; Abiodun Elegbeleye, Femi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8971

Abstract

Stock market price prediction is a challenging task that plays a crucial role in investment decision-making and financial risk management. Traditional approaches often rely on a single machine learning (ML) algorithm for predictive modeling. In this contribution, an innovative framework that integrates logistic regression (LR) with support vector machine (SVM) to improve the accuracy and reliability of stock market price prediction. Combining the strengths of both algorithms, the proposed model harnesses the interpretability of LR and the robustness of SVM to capture complex relationships in stock market data. Experiments conducted on publicly available Yahoo Finance stock dataset and the Dhaka dataset, the results show that the proposed model yielded accuracies of 97.15% and 98.86% respectively. In comparison with other models, the proposed method outperformed the other models in terms of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and accuracy. The contribution and importance of leveraging hybrid modelling techniques to enhance stock market price prediction and facilitate informed investment decision-making.
A method for brand image recognition for ordering payment in supermarket Huynh, Nhat Nam; Tran, Tan Hai Bui; Nguyen, Quyen
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9533

Abstract

This paper presents a product brand recognition method based on the YOLOv8 algorithm. The performance evaluation of the proposed method is conducted on two datasets consisting of GroZi-120 and GroZi-3.2K. The results show that the proposed method can achieve high accuracy. The precision and F1-score on the GroZi-120 and GroZi-3.2K datasets reach of {74.77%, 80%} and {99.86%, 100%}, respectively. The comparison with previous studies shows that the precision and F1-score obtained by the YOLOv8 method outperform some previous studies. Additionally, the effectiveness of the proposed method is also evaluated on a dataset of 6,170 images for twelve real products collected from supermarkets for use in order payment. The results show that the proposed method can be applied in single-order payment as well as multiple simultaneous orders with high accuracy in product recognition ranging from 94% to 98%. Therefore, the proposed method can be applied in order quick payment at supermarkets.
Enhanced Semarang batik classification using deep learning: a comparative study of CNN architectures Winarno, Edy; Solichan, Achmad; Putra Ramdani, Aditya; Hadikurniawati, Wiwien; Septiarini, Anindita; Hamdani, Hamdani
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9347

Abstract

Batik is an important part of Indonesia’s cultural heritage, with each region producing unique designs. In Central Java, Semarang is known for its distinctive batik patterns that reflect rich local traditions. However, many people are still unfamiliar with these designs, which threatens their preservation. This study develops an automated system to classify Semarang batik patterns, showing how technology can help safeguard cultural heritage. A convolutional neural network (CNN) approach was used to recognize ten batik types, including Asem Arang, Asem Sinom, Asem Warak, Blekok, Blekok Warak, Gambang Semarangan, and Kembang Sepatu. Pre-processing steps—such as image resizing, cropping, flipping, and rotation—improved model performance and reduced complexity. Five CNN architectures (MobileNetV2, ResNet-50, DenseNet-121, VGG-16, and EfficientNetB4) were tested using 224×224 input size, Adam optimizer, ReLU activation, and categorical cross-entropy loss. Results show VGG-16, ResNet-50, and DenseNet-121 achieved perfect accuracy (1.0) on a dataset of 3,000 locally collected images. These findings highlight CNN models’ strong potential for batik pattern recognition, supporting digital preservation of Indonesian culture.
Driver activity recognition using deep learning based on multi-step batch size up Utomo, Darmawan; Indria Prambodo, Natanael; Murtianta, Budihardja
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9069

Abstract

The increasing popularity of electric motorbikes in Indonesia, while promoting sustainable mobility, also raises concerns regarding traffic safety. Given the high incidence of motorcycle-related accidents, there is a critical need for systems capable of monitoring and recognizing driver behavior. This study proposes a driver activity recognition system for electric motorbikes, utilizing an event data recorder (EDR) to capture seven key sensor signals: three-axis acceleration, voltage, current, power, and speed. A custom dataset was constructed using data collected from 10 subjects, each performing five driving activities including forward drive, brake, stop, left turn, and right turn for over three-minute intervals per activity. The classification model is based on a long short-term memory (LSTM) neural network. To optimize training efficiency, a multi-step batch size up (MSBU) strategy was introduced, which accelerates training time by 1.84× compared to a fixed batch size of 32. The best performance was achieved using a segment length of 75 time-steps, yielding an accuracy and macro F1-score of 0.9873. These results demonstrate the effectiveness of the proposed system for real-time driver behavior monitoring and activity recognition in electric motorbike applications.
Enhancing skin cancer detection using transfer learning and AdaBoost: a deep learning approach Listiyono, Hersatoto; Retnowati, Retnowati; Purwatiningtyas, Purwatiningtyas; Nur Wahyudi, Eko; Maskur, Ali
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10379

Abstract

Skin cancer is one of the most prevalent types of cancer worldwide, with early detection playing a critical role in improving patient outcomes. In this study, we propose a deep learning model based on LeNet-7 combined with adaptive boosting (AdaBoost) to classify skin lesions as either benign or malignant using the International Skin Imaging Collaboration (ISIC) dataset. We evaluate the proposed model alongside other well-established deep learning architectures, such as residual network (ResNet), VGGNet, and the traditional LeNet model, through various performance metrics including precision, recall, F1-score, specificity, Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and testing accuracy. Our results demonstrate that the proposed model (LeNet-7+AdaBoost) significantly outperforms the other models, achieving a testing accuracy of 91.3%, precision of 0.92, recall of 0.91, and AUC-ROC of 0.93. The model successfully addresses issues of overfitting and generalization, providing a robust solution for skin cancer classification. However, some misclassifications of visually similar benign and malignant lesions highlight areas for future improvement. The proposed model shows promise in real-world medical applications and paves the way for further research into optimizing deep learning models for skin cancer detection.
Evaluating the impact of risk management and cybersecurity on decision-making in the Peruvian National Informatics System Estrada, Frank Agustín Olivos; Flores-Sotelo, Willian Sebastian; Barrera-Avalos, Carmen Rosa; Martínez-Aberga, Williams Arturo; Sulca-Guillen, Richard; Chávez-Díaz, Jorge Miguel
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9781

Abstract

This research addresses the influence of risk management (RM) and cybersecurity (CIB) on decision making (DM) of the Peruvian State's National Informatics System (SNIEP) in the year 2024, in line with sustainable development goal 9. Using a quantitative, non-experimental, cross-sectional design approach, a sample of 487 CIB analysts was analyzed to explore the relationship between these critical variables. The findings show uneven implementation of RM and CIB practices, which significantly impact the DM and quality of information (Sig 0.000), processes (Sig 0.001), and the effectiveness of system decisions (Sig 0.060). In addition, key areas were identified to strengthen the integration of RM and CIB strategies in the state's digital environment, highlighting their importance to ensure informed and resilient decisions in the face of growing cyber threats. The study provides empirical evidence on their impact on the quality, effectiveness and security of DM in government digital environments. The research contributes both to the development of a theoretical framework that articulates concepts of RM, CIB, and DM in the public sector, and to the formulation of strategies and policies that promote a secure and efficient digital infrastructure, aimed at improving public services and citizen trust in the contemporary digital environment.
Optimized XGBRF-CatBoost model for accurate polycystic ovary syndrome prediction using ultrasound imaging Annamalai, Boobalan; Periyasamy, Sudhakar
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10237

Abstract

Polycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder characterized by hyperandrogenism, anovulation, oligomenorrhea, and ovarian microcysts, often resulting in infertility, obesity, and dermatological issues. This study proposes a hybrid machine learning (ML) framework for accurate PCOS prediction using ovarian ultrasound imaging and clinical parameters. A gradient regression-based multilayer perceptron neural network (GRMPNN) is employed for feature selection, followed by a stacked ensemble classifier combining extreme gradient boosted random forest (XGBRF) and CatBoost for final diagnosis. The dataset comprises 541 anonymized patient records from Ghosh Dastidar Institute for Fertility Research (GDIFR), incorporating 45 clinical, hormonal, and imaging features. Preprocessing includes normalization, noise reduction, and random oversampling to address class imbalance. Feature selection using univariate statistical testing and chi-square ranking identified 13 key attributes. The proposed XGBRF–CatBoost model achieved accuracy, precision, recall, and F1-score exceeding 98% across both benchmark datasets, outperforming principal component analysis (PCA) and neural fuzzy rough subset evaluating (NFRSE)-based models. This framework enhances diagnostic precision, reduces computational complexity, and supports scalable integration into clinical workflows. The findings underscore the potential of artificial intelligence (AI)-assisted tools in reproductive medicine and present a reproducible, interpretable approach for early PCOS detection.
Feasibility study of solar-diesel generation hybrid power systems: a case study of rural electrification in Papua, Indonesia Liga, Marthen; Sampe, Aris; Jonatan Numberi, Johni
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9589

Abstract

This study presents a contextually tailored off-grid hybrid energy system consisting of solar photovoltaic (PV), diesel generator (DG), and battery storage, designed for the remote mountains village of Jifak in Papua, Indonesia. The objective is to evaluate the technical, economic, environmental, and social feasibility of electrification in underserved regions. A comprehensive feasibility analysis was conducted using HOMER software, incorporating realistic communal load profiles, National Aeronautics and Space Administration (NASA) climate data, and field-based social assessments. The optimized system achieved a net present cost (NPC) of $10.43 million, a levelized cost of energy (LCOE) of $0.5095/kWh, and annual emissions of 7,965 kg CO₂. Sensitivity analysis was performed on fuel cost, discount rate, and inflation rate to assess system robustness. Beyond the technical metrics, the study assessed the socio-economic impacts of electrification, revealing improvements in lighting quality, education, productivity, income generation, and environmental awareness. The findings provide a replicable model and decision-making framework for policymakers and practitioners aiming to deploy low-carbon, sustainable electrification strategies in similarly remote regions.

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