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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 73 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 73 Documents clear
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.
Enabling SECS/GEM in legacy equipment: a proof of concept Syahir Kamal Fitri, Muhammad; Manickam, Selvakumar; Ul Arfeen Laghari, Shams; Kok Chia, Siang; Khairi Ishak, Mohamad; Karuppayah, Shankar
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.v14i5.9516

Abstract

The rapid adoption of Industry 4.0 (I4.0) has driven the need for automated machine-to-machine (M2M) communication in manufacturing. However, legacy equipment remains a challenge due to its incompatibility with modern protocols like semiconductor equipment and materials international (SEMI) equipment communication standard/generic equipment model (SECS/GEM). Replacing these machines is costly, making retrofitting a more viable solution. This paper proposes a modular automation software framework that enables SECS/GEM integration for legacy machines without extensive hardware modifications. The system is implemented using Raspberry Pi and Arduino, acting as an intermediary between legacy equipment and modern factory networks. The framework facilitates real-time data exchange, remote monitoring, and enhanced automation while ensuring scalability and cost-effectiveness. Experimental evaluation demonstrates improved interoperability and reduced manual intervention. This solution provides a practical and adaptable approach to integrating legacy systems into (I4.0) environments.
MAS-TENER: a modified attention score transformer encoder for Indonesian skill entity recognition Nonsi Tentua, Meilany; Suprapto, Suprapto; Afiahayati, Afiahayati
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.v14i5.9731

Abstract

Skill entity recognition is a crucial task for aligning educational curricula with the evolving needs of the industry, particularly in multilingual job markets. This study introduces modified attention score transformer encoder (MAS-TENER), a novel transformer-based model designed to enhance the recognition of skill entities from Indonesian job descriptions. The proposed model modifies the attention mechanism by integrating relative positional embeddings and removing the scaling factor in self-attention. These improvements enhance the context of tokens, allowing for the accurate establishment of hard skills, soft skills, and technology skills. The MAS-TENER model was pre-trained and fine-tuned using a combinF.ation of job description datasets and additional corpora, achieving an F1-score of 90.46% at the entity level. The experimental results demonstrate the model's ability to handle unstructured, mixed-language job descriptions, with significant potential for curriculum reform and the development of new workforce capabilities. The study demonstrates the efficacy of the MAS-TENER model as an effective response for any natural language processing (NLP) task in low-resource languages. Moreover, the scope of long-term job market analytics in action research has been a key skill set in the education policy arena, demonstrating collaborative workforce capabilities.
Explainable artificial intelligence for multiclass prediction model of suicide attempt Nordin, Noratikah; Noor, Mohd Halim Mohd; Zainol, Zurinahni; Fong, Chan Lai; Buji, Ryna Imma
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.v14i5.9837

Abstract

Suicide attempt prediction is a challenging classification problem that involves a variety of risk factors in individuals with various medical conditions. Accurate risk stratification prediction is hampered by the absence of reasons for those who have attempted suicide and developing prediction model is challenged to be explained. Therefore, this work aimed to develop a multiclass prediction model for suicide attempts and to use Shapley additive explanations (SHAP), an explainable artificial intelligence (XAI) method to analyze the prediction model for suicide attempts in explaining the decision of the model. The prediction model is trained using machine learning approaches, random forest (RF) and gradient boosting (GB), on a clinical dataset of patients with chronic diseases. GB demonstrated higher accuracy with 0.81 than RF with 0.78 for multiclass classification results (no risk, low risk, moderate risk, and high risk). By analyzing the SHAP explanations, clinicians can gain valuable insights into the factors contributing to suicide attempt predictions in patients with chronic diseases. This enhanced understanding can facilitate more informed and appropriate treatment decisions, potentially leading to improved patient outcomes and targeted interventions.
Design and development of harmonic filters for harmonics reduction in polluted distribution network R. Chavan, Pranita; R. Patil, Babasaheb
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.v14i5.9504

Abstract

Recently due to development in the power electronics sector, there is a tremendous increase in nonlinear loads. These nonlinear loads cause distortion in the system current and result in degrading quality of power. The poor power quality causes technical and financial losses in the system which necessitates adoption of techniques to reduce the harmonic distortion to meet IEEE-standards and improve system efficiency. As per literature, passive, active and hybrid filter techniques are implemented to mitigate the harmonics. Each has merits and demerits. Constructive reduction in current harmonics improves the life and efficiency of equipment’s also assists to improve power quality and relieves penalties imposed by utilities. In this work, an attempt is made to give a detailed approach used in the designing of harmonic filters. This study will provide a broad outline to the engineer, researcher and consultant working in the field of power quality to design filters for the case under study. The steps to design the filters are well explained with mathematical equations and examples for greater insight. To validate the performance of the filter a MATLAB/Simulink platform is utilized. The outcome of the simulation result proved that current harmonics are minimized with a substantial amount.

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