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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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Overcurrent effects on copper insulated PVC cables and fire resistance via thermal imaging and macrostructure analysis Ali Akbar, Muhammad; Humaidi, Syahrul; Tarigan, Kerista; Ramdan, Dadan; Frida, Erna; Siregar, Yulianta
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8628

Abstract

This study investigates the effects of overcurrent on copper (Cu) insulated polyvinyl chloride (PVC) cables, focusing on their thermal behavior and fire resistance. We utilized thermal imaging, macrostructural analysis, and Joule heating calculations to evaluate six cable samples subjected to various currents. Results showed that with increasing current, the temperature of the cables rose significantly. For example, the CC0 sample, with no current, had a temperature of 36 °C, while the CC110 sample, subjected to 110 A, reached 1,091 °C. Joule heating calculations indicated energy values ranging from 0 J for the CC0 sample to 7,260,000 J for the CC110 sample. Physical observations included minor deformations at 253 °C and complete insulation loss at 1,091 °C. These findings emphasize the critical need for managing overcurrent to prevent severe cable damage and enhance system safety. This research provides practical insights for optimizing cable design and improving thermal management, offering valuable contributions to electrical engineering practices.
Glioma segmentation using hybrid filter and modified African vulture optimization Kuntiyellannagari, Bhagyalaxmi; Dwarakanath, Bhoopalan
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8730

Abstract

Accurate brain tumor segmentation is essential for managing gliomas, which arise from brain and spinal cord support cells. Traditional image processing and machine learning methods have improved tumor segmentation but are often limited by accuracy and noise handling. Recent advances in deep learning, particularly using U-Net and its variants, have achieved significant progress but still face challenges with heterogeneous data and real-time processing. This study introduces a hybrid bilateral mean filter for noise reduction coupled with an ensemble deep learning model that integrates U-Net, InceptionV2, InceptionResNetV2, and W-Net to enhance segmentation accuracy and efficiency. Additionally, we propose a novel modified African vulture optimization algorithm (MAVOA) to further refine segmentation performance. Evaluated on the BraTS 2020 dataset, our model achieved a loss of 0.023 with strong performance metrics: 98.2% accuracy, 97.2% mean intersection over union (IOU), and 99.1% precision. It effectively segmented glioma subregions with dice scores of 0.96 for necrotic areas, 0.97 for edema, and 0.91 for enhancing regions. On the BraTS 2021 dataset, the model maintained high accuracy 96.4%, mean IOU 95.9%, and dice coefficients of 0.91 for necrotic areas, 0.95 for edema, and 0.92 for enhancing regions.
Enhancing SDN security with a feature-based approach using multiple k-means, Word2Vec, and neural network Yzzogh, Hicham; Benaboud, Hafssa
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8834

Abstract

In the rapidly evolving landscape of network management, software-defined networking (SDN) stands out as a transformative technology. It revolutionizes network management by decoupling the control and data planes, enhancing both flexibility and operational efficiency. However, this separation introduces significant security challenges, such as data interception, manipulation, and unauthorized access. To address these issues, this paper investigates the application of advanced clustering and classification algorithms for anomaly detection and traffic analysis in SDN environments. We present a novel approach that integrates multiple k-means clustering models with Word2Vec for feature extraction, followed by classification using a neural network (NN). Our method is rigorously benchmarked against a traditional NN model to comprehensively evaluate performance. Experimental results indicate that our approach outperforms the NN model, achieving an accuracy of 99.97% on the InSDN dataset and 98.65% on the CIC-DDoS2019 dataset, showcasing its effectiveness in detecting anomalies without relying on feature selection. These findings suggest that integrating clustering techniques with feature extraction algorithms can significantly enhance the security of SDN infrastructures.
Optimized convolutional neural network deep learning for Arabian handwritten text recognition Ritonga, Mahyudin; L. Bangare, Manoj; Manoj Bangare, Pushpa; L. Bangare, Sunil; Sachin Vanjire, Seema; Moholkar, Kavita; Kasat, Kishori; Rozak, Purnama
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7696

Abstract

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.
Exploring 5G network performance: comparison of inner and outer city areas in Phetchaburi Province Pornpongtechavanich, Phisit; Daengsi, Therdpong
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8693

Abstract

The advancement of 5G technology has transformed various aspects of life, including tourism, by enabling people worldwide to communicate and travel with ease. Traveling to different places and countries is now seamless, removing language barriers and facilitating easy access to information on culture, accommodation, and tourist attractions. Additionally, access to applications that facilitate quicker language translation further enhances the travel experience. Phetchaburi Province holds significant importance as a global tourist destination. United Nations Educational, Scientific, and Cultural Organization (UNESCO) has recognized Phetchaburi as a member of the UNESCO creative cities network (UCCN), comprising one of 49 cities worldwide acknowledged for their creative city initiatives. Phetchaburi Province stands as the 5th city in Thailand to receive this designation. This research investigated 5G performance in Phetchaburi Province, both the inner and outer city, focusing on download and upload speeds. The results indicate that there is widespread 5G coverage throughout Phetchaburi Province, including urban and rural areas, especially for the 5G network with a good performance provided by one of the mobile network operators (MNOs). In addition, the statistical analysis reveals differences in 5G performances between the inner city and the outer city of Phetchaburi Province, particularly for download speeds (p-value0.001).
Heart disease detection using machine learning Al-Habahbeh, Mohammad; Alomari, Moath; Khattab, Hebatullah; Alazaidah, Raed; Alshdaifat, Nawaf; Abuowaida, Suhaila; Alqatan, Saleh; Arabiat, Mohammad
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8324

Abstract

Heart disease continues to be a major worldwide health issue, requiring accurate prediction models to improve early identification and treatment. This research aims to address two main objectives in light of the increasing prevalence of heart-related disorders. Firstly, it aims to determine the most efficient classifier for identifying heart disease among twenty-nine different classifiers that represent six distinct learning strategies. Furthermore, the research seeks to identify the most effective method for selecting features in heart disease datasets. The results show how well different classifiers and feature selection methods work by using two datasets with different features and judging performance using four important criteria. The evaluation results demonstrate that the RandomCommittee classifier outperforms in diagnosing heart illness, displaying strong skills across various learning strategies. This classifier exhibits favorable results in terms of accuracy, precision, recall, and F1-score metrics, hence confirming its appropriateness for predictive modeling in heart-related datasets. Moreover, the paper examines feature selection methods, specifically aiming to determine the most effective method for enhancing the predicted accuracy of heart disease models. The prediction models' overall performance is enhanced by their capacity to accurately identify and prioritize pertinent variables, thereby facilitating the early detection and management of heart-related problems.
Factorized cross entropy integrated hyperspectral CNN (HSCNet-FACE) for hyperspectral image classification C. Patil, Pawankumar; Sonnad, Shashidhar
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8829

Abstract

The use of hyperspectral image classification algorithms has garnered increasing interest from the scientific community in recent years, especially in the field of geosciences for pattern recognition applications. In order to extract full spectral-spatial characteristics, this study presents feature extraction with hyperspectral CNN (HSCNet), a unique hierarchical neural network architecture. HSCNet can handle computational complexity issues and capture extensive spectral-spatial information with ease. We use factorized cross entropy (FACE) to address the common problem of class imbalance in both experimental and real-world hyperspectral datasets in order to construct an accurate land cover classification system. FACE makes it easier to reconstruct the loss function, which helps to effectively accomplish the goals that have been expressed. We provide a new framework for hyperspectral image (HSI) classification called FACE, which combines components from HSCNet and FACE. Next, we carry out in-depth studies using two different remote sensing datasets: Botswana (BS) and Indian Pines (IP). We compare the effectiveness of different backbone networks in terms of categorization and compare its classification performance under various loss functions. Comparing our suggested classification system against the state-of-the-art end-to-end deep-learningbased techniques, we find encouraging results
Benchmarking machine learning algorithm for stunting risk prediction in Indonesia Novalina, Nadya; Aksar Tarigan, Ibrahim Amyas; Kayla Kameela, Fatimah; Rizkinia, Mia
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8997

Abstract

Stunting is a condition caused by poor nutrition that results in below-average height development, potentially leading to long-term effects such as intellectual disability, low learning abilities, and an increased risk of developing chronic diseases. One effort to reduce stunting is to apply a machine learning algorithm with a data science approach to develop risk prediction models based on factors in stunting. The study used the current cross industry standard process for data mining (CRISP-DM) framework to gain insight and analyzed 1561 records of data collected from the Indonesia family life survey (IFLS) for the prediction models. Two sampling methods, random undersampling, and oversampling synthetic minority oversampling technique (SMOTE), were employed and compared to overcome the data imbalance problem. Four machine learning classifier algorithms were trained and tested to determine the best-performing model. The experiment results showed that the algorithms yielded an average accuracy of more than 75%. Using the undersampling technique, the accuracy obtained by logistic regression, k-nearest neighbor (KNN), support vector classifier (SVC), and decision tree classifier were 95.21%, 78.91%, 92.97%, and 86.26% respectively. Meanwhile, the oversampling technique reached 96.17%, 88.50%, 93.29%, and 95.21%, respectively. Logistic regression emerges as the best classification, with oversampling yielding superior performance.
An economical approach of structural strength monitoring utilizing internet of things S. Nayak, Deekshitha; Kumar Pandey, Anubhav
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8752

Abstract

In the current environment, structural health monitoring (SHM), has become increasingly important. The cost of sensors and connectivity has significantly decreased, allowing for remote data gathering for critical analysis and structure monitoring. This allows for the assessment and improvement of the structures' residual lifespan. The internet of things (IoT) is a network of intelligent sensors that combines the identification and detection followed by sending the different structural responses to remote computers for further analysis i.e., processing and monitoring. In this work, an integrated IoT platform for damage detection is proposed which includes an Arduino, Wi-Fi module, and sensors. The sensors gather responses from the host structure which follows a precise mathematical model is introduced to determine and measure the structural damage in comparison to the reactions of the structural member that is in good health. To determine the degree of damage, the responses recorded from the damaged and healthy beams are analyzed using the cross-correlation (CC) damage index. Moreover, the analysis carried out reveals the CC values are uploaded to the cloud, where, if the CC value is over the threshold limit, a mobile warning message is delivered.
Optimizing fatigue life predictions for scraper rings: classical vs modern models Fatihi, Sophia; El Hasnaoui, Yassine; Ouabida, Elhoussaine; Mharzi, Hassan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9256

Abstract

This study provides a comprehensive comparative evaluation of classical and modern predictive models for fatigue life in scraper rings of internal combustion engines, which operate under high thermo-mechanical stresses. Accurate fatigue life predictions are essential for optimizing engine component design, preventing both over- and under-engineering while ensuring long-term reliability. The effectiveness of both traditional models and newer advanced approaches was analyzed using loading profiles that replicate real-world engine operating conditions. Results indicate that stress-life models offer more reliable predictions for high-cycle fatigue scenarios, while strain-life models perform better under low-cycle fatigue conditions. Furthermore, fracture mechanics models show great promise in predicting crack propagation and identifying failure mechanisms. Detailed inspections and Légraud-Poirier (LP) tests confirmed fatigue-induced cracking at critical locations of the scraper rings, emphasizing the importance of incorporating multi-axial loading in fatigue assessments. The findings underscore the necessity for using comprehensive loading profiles and thorough inspections to enhance the accuracy and dependability of fatigue life predictions, which are critical for improving the performance and durability of engine components.

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