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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 75 Documents
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Search results for , issue "Vol 14, No 2: April 2025" : 75 Documents clear
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.
Wireless sensor network using nRF24L01+ for precision agriculture Abidin, Zainul; Falah, Raisul; Setyawan, Raden Arief; Wardana, Fitri Candra
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.v14i2.8481

Abstract

Precision agriculture is a strategy for varying inputs and cultivation methods to suit varying soil conditions and agricultural crops. In order to optimize precision agriculture, wireless sensor network (WSN) is suitable to be integrated. In this research, network devices that communicate using nRF24L01+ based WSN was proposed. As a prototype, four sensor nodes were employed to measure the parameters of air temperature and humidity, soil moisture, and power supply voltage. While, a sink node serves to store measurement data locally. The data are sent to the sink node with a mesh network topology and saved in a comma-separated values (CSV) file and local database. Experimental results show that each sensor node can measure all parameters and successfully send data to the sink node every 1 minute without losing the data. The mesh topology can route data transfer automatically. Round trip time (RTT) of each sensor node depends on the distance from each node. Average power consumption of all sensor nodes in send mode is between 84 mW and 90 mW. Meanwhile, in sleep mode, the sensor nodes 1 and 2 consumed around 21-22 mW and the sensor nodes 3 and 4 consumed around 30 mW which are lower than the send mode.
Optimizing plant health monitoring: improved accuracy and the computational efficiency with stacked machine learning models and feature filtering Sangeetha, Tupili; Ezhumalai, Periyathambi
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.v14i2.8809

Abstract

Plant cultivation can be effectively achieved with the help of hydroponic farming that allows growing soilless and organic plant veggies. However, maintaining optimal plant health in such controlled environments requires continuous monitoring and assessment techniques. This paper provides a comprehensive description of how to determine and categorize the health of hydroponic plants based on a wide range of parameters, such as temperature, pH, electrical conductivity (EC), leaf count, plant height, and vegetative indices. We present a novel approach termed “Hybrid XGBoosting” that combines the multi-classification algorithm extreme gradient boosting (XGBoost) with gradient-based one-side sampling (GOSS) methods to improve accuracy and processing efficiency. This approach first adopts a feature correlation method known as “Pearson’s correlation” for reducing repeated data that are directly proportional or inversely proportional to each other. Finally, we perform a thorough comparative study using well-known algorithms including traditional XGBoost, AdaBoost, and gradient boosting. We demonstrate the better prediction capabilities of Hybrid XGBoosting with 97.93% accuracy through rigorous testing and evaluation, showing its potential for improving hydroponic plant health assessment approaches. Additionally, our research employs comprehensive algorithm assessment measures, such as root mean squared scaled error (RMSSEE), to guarantee the stability and reliability of the results.
A guide for selection of wireless communication technology for effective and robust early forest fire detection system Salaria, Anshika; Singh, Amandeep
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.v14i2.8613

Abstract

The world is facing a major ecosystem crisis due to global warming and pollution. Considering the rate at which the temperatures are rising, one must think about the causes and origins of this extreme environmental shift. Today, countries like India, have cities ranked as some of the most polluted cities in the world. Apart from vehicular traffic and industrial wastes, one of the prime components of the entire problem is forest fires. Burning forests emit tons of harmful gases into the atmosphere. This disaster also leaves drastic aftereffects on the economy and society. Therefore, an efficient system should be designed to monitor the forest fires at the earliest. Highlighting the role of wireless sensor networks in the irregular terrains of forests and considering the technical challenges, it is important to identify, first, the best technology for communication among sensors, in such complex terrains. Second, the identification of an optimization algorithm for the deployment of sensors to achieve maximum coverage This work presents an analysis of state-of-the-art wireless sensor networks to identify a reliable communication technique. Further an optimization algorithm is proposed for maximum coverage with a minimum number of sensors. The algorithm outperforms the other state-of-the-art algorithms in simulation results.
Shallot disease classification system based on deep learning Lidyawati, Lita; Darlis, Arsyad Ramadhan; Munawaroh, Sofa Jauharotul
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.v14i2.8498

Abstract

Shallot is one of the important horticultural commodities for society and has high economic value. The problem with shallot cultivation is disease attacks on plants, one of which is Fusarium wilt. With the condition that the shallot commodity at the farmer level has a high failure rate, it is hoped that this research can assist farmers in providing information about shallot plants that have diseased plant characteristics using deep learning system convolutional neural network (CNN) method by utilizing leaf images on shallot plants. This research was conducted using the ResNet-18 architecture, with a total of 400 data in the dataset divided into 2 categories, namely healthy and diseased Fusarium wilt. The device used to carry out the classification process in this research is a Jetson Nano 2 GB. The ratio used to form a model from the dataset is 80-20 (80% training data and 20% validation data). The accuracy results for the classification of shallot plant diseases using real-time leaf images during the day have an average accuracy value of 68% on healthy plants and 62% on Fusarium wilt plants, while at night it has an average accuracy value of 53% on healthy plants and 47% on Fusarium wilt plants.

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