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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 13, No 6: December 2024" : 75 Documents clear
Hybrid approach to medical decision-making: prediction of heart disease with artificial neural network Bhavekar, Girish Shrikrushnarao; Chafle, Pratiksha Vasantrao; Goswami, Agam Das; Marathula, Ganesh Kumar; Hirve, Sumit Arun; Karpe, Suraj Rajesh; Magar, Nitin Sonaji; Farakte, Amarsinh Baburao; Pikle, Nileshchandra Kalbarao; Shinde, Snehal Bankatrao; Gaikwad, Amit Kamalakar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.5583

Abstract

Heart disease prediction is important in today’s world because it helps to reduce the unpredictable death rate of patients, and cardiac diseases are considered one of the most serious diseases affecting people. Hence, in this paper, a heart disease prediction model is designed for effective prediction of heart diseases by means of machine learning (ML) and deep learning (DL). This prediction uses the proposed method of an artificial neutral network and the Chi2 feature selection method applied to determine which features from the dataset were suitable for prediction. The proposed methodology uses classifiers like support vector machines (SVM), Naive Bayes (NB), logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Python was used to conduct the study that assessed the ANN system proposal with the Cleveland heart disease dataset at the University of California (UCI). Compared to other algorithms, the model achieves an accuracy of 97.64% and takes 0.49 seconds to execute, making it superior in predicting heart disease.
Enhance the accuracy of malicious uniform resource locator detection based on effective machine learning approach Alqahtani, Haifa; Abu-Khadrah, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7797

Abstract

Phishing attacks are increasing with the rise in web users. Addressing them requires understanding the techniques and employing effective response strategies. Phishing websites mimic authentic ones to deceive users into divulging personal information like bank account details, national insurance numbers, and passwords. Therefore, victims face financial loss from breached information security, constituting high-level internet fraud. Detecting phishing websites necessitates an intelligent model capable of recognizing suspicious features. To that purpose, this paper examines three classification methods for detecting phishing website attacks. This analysis allows to reconsider our awareness of phishing attacks and prevent the damage caused by phishing attempts in advance. Phishing website detection algorithm using three classification algorithms is proposed in this paper. It achieves high phishing website detecting accuracy, because three classification algorithms random forest (RF), support vector machine (SVM), and Bagging are combined in one system. The result of this research is found accuracy on validation set is 92.33%, the precision on validation set is 92.13%, the recall is 92.09% and F1 score is 92.10%. That prove that the result obtained in this research is more accurate than all the results of all the algorithms were applied in the same dataset that was train the proposed algorithm on it.
Classification of human grasp forces in activities of daily living using a deep neural network Padilla-Magaña, Jesus Fernando; Sanchez-Suarez, Isahi; Peña-Pitarch, Esteban
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7181

Abstract

The study of human grasp forces is fundamental for the development of rehabilitation programs and the design of prosthetic hands in order to restore hand function. The purpose of this work was to classify multiple grasp types used in activities of daily living (ADLs) based on finger force data. For this purpose, we developed a deep neural network (DNN) model using finger forces obtained during the performance of six tests through a novelty force sensing resistor (FSR) glove system. A study was carried out with 25 healthy subjects (mean age: 35.4±11.6) all right handed. The DNN classifier showed high overall performance, obtaining an accuracy of 93.19%, a precision of 93.33%, and a F1-score of 91.23%. Therefore, the DNN classifier in combination with the FSR glove system is an important tool for physiotherapists and health professionals to determine and identify finger grasp forces patterns. The DNN model will facilitate the development of tailored and personalized rehabilitation programs for subjects recovering of hand injurie and other hand diseases. In future work, prosthetic hand devices can be optimized to more accurately reproduce natural grasping patterns.
Applying genetic algorithm for optimizing return loss of proximity coupled microstrip antenna Chemachema, Karima; Ikhlef, Ismahene
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7679

Abstract

The proximity-coupled rectangular microstrip antenna (PRMSA) is optimized using the genetic algorithm (GA) to improve key parameters such as input impedance, return loss, and voltage standing wave ratio (VSWR). Fitness functions for the GA program have been developed using the transmission-line method (TLM) to analyze the PRMSA. The stochastic search capabilities of GA address electromagnetic characteristics that are challenging for other optimization techniques. In this study, GA optimization technique has been utilized for the PRMSA; this antenna is optimized for its parameters as length of the patch, thickness, width and length of strip line in order to achieve better return loss. According to the existing results for calculating S11, we arrived at the smallest and best value (-28 dB) using GA compared to previous works using other methods. Further analysis is provided on how various antenna parameters affect performance. The GA was executed for 100 generations, with the optimized results enhancing the antenna’s efficiency. The computed results closely match the experimental data, and the accuracy of these results supports the effectiveness of using GA.
Experimental study the performance of a 6-bladed Savonius vertical axis wind turbine using polyvinyl chloride material Siregar, Izhary; Dwi Nugroho, Setyawan; Zaskia Pratiwi, Citra; Mawardi, Iman; Jibril, Ahmad; Suhadi, Suhadi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7550

Abstract

The Savonius U type wind turbine is a vertical axis turbine that can operate at low wind speeds. In general, the performance of this turbine is influenced by several factors, one of which is the shape of the turbine blade. This research aims to test the design results of a 6 blade Savonius turbine with a blade length of 50 cm made from polyvinyl chloride (PVC) by varying the dimensions of the blade diameter. The variables that vary between blade length and blade diameter are D/L=0.10, D/L=0.13, D/L=0.18, and D/L=0.20. The aim of this research is to determine the effect of variations in the parameters above on turbine rotation and the electrical power produced in a direct current (DC) generator at each variation in wind speed. From the research results, it is known that the trend graph of the relationship between turbine rotation and wind speed has a linear correlation. In simple terms, this turbine can be applied to DC voltage loads such as lighting using light emitting diode (LED) lamps with a maximum power capacity of ± 16 watts, while the overall efficiency (OE) is 50.25%.

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