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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.
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Articles 75 Documents
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Search results for , issue "Vol 13, No 5: October 2024" : 75 Documents clear
Automatic prediction of learning styles: a comprehensive analysis of classification models Lestari, Uning; Salam, Sazilah; Choo, Yun-Huoy; Alomoush, Ashraf; Al Qallab, Kholoud
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7456

Abstract

Learning styles are a topic of interest in educational research about how individuals acquire and process information in offline or online learning. Identification of learning styles in the online learning environment is challenging. The existing approaches for the identification of learning styles are limited. This study aims to review the many learning styles characterized by various classification approaches toward the automatic prediction of learning styles from learning management system (LMS) datasets. A systematic literature review (SLR) was conducted to select and analyze the most pertinent and significant papers for automatically predicting learning styles. Fifty-two research papers were published between 2015-2023. This research divides analysis into five categories: the classification of learning style models, the collection of the collected dataset, learning styles based on the curriculum, research objectives related to learning styles, and the comprehensive analysis of learning styles. This study found that learning style research encompasses diverse theories, models, and algorithms to understand individual learning preferences. Statistical analysis, explicit data collection, and the Felder-Silverman model are prevalent in research, highlighting the significance of algorithm improvement for optimizing learning processes, particularly in computer science. The categorization and understanding of various methods offer valuable insights for enhancing learning experiences in the future.
Development and implementation of a low-cost metal detector device Salah, Wael A.; Shabaneh, Arafat A. A.
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7613

Abstract

Metal detectors contribute to safety, protection, and detection in a variety of disciplines by locating and identifying metal items, playing an important role in which the metal detectors appear in security, archaeology, and industrial applications respectively. The necessity for identifying different types of metals and the need for a high level of security system led to the need of affordable and sensitively metal detecting devices. In this paper, the magnetic pulse induction (PI) technology is used in the development of metal detectors. The primary control circuit is utilizing an Arduino controller which allows the input signal’s to be controlled and monitored using a liquid-crystal display (LCD) and mobile application. A voltage sensor for measuring the analog output from the circuit and capturing the information to the Arduino by employing a Bluetooth module. The Arduino controller estimate the percentage of the signal’s strength and display it on the LCD. Simultaneously, the signal could be sent to the mobile application through Bluetooth in order for the application to display the strength in the form of a spectrum of colors. The results of testing applied to the proposed prototype reveal that the system is running with a satisfactory accuracy and sensitivity.
Feature selection in P2P lending for default prediction using grey wolf optimization and machine learning Sam'an, Muhammad; Safuan, Safuan; Munsarif, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7651

Abstract

Online loan services like peer-to-peer (P2P) lending enable lenders to transact without bank intermediaries. Predicting which lenders are likely to default is crucial to avoid bankruptcy since lenders bear the risk of default. However, this task becomes challenging when the P2P lending dataset contains numer- ous features. The prediction performance could be improved if the dataset fea- tures are relevant. Hence, applying feature selection to remove redundant and irrelevant features is essential. This paper introduces a novel wrapper feature selection model to identify the optimal feature subset for predicting defaults in P2P lending. The proposed method includes two main phases: feature selection and classification. Initially, grey wolf optimization (GWO) is used to select the best features in P2P lending datasets. Then, the fitness function of GWO is as- sessed using ten different machine learning (ML) models. Experimental results indicate that the proposed model outperforms previous related work, achieving average accuracy, recall, precision, and F1-score of 96.77%, 80.73%, 97.52%, and 80.06%, respectively.
Novel entropy-based style transfer of the object in the content image using deep learning Raghatwan, Jyoti Sudhakar; Arora, Sandhya
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7659

Abstract

Recently neural style transfer (NST) has drawn a lot of interest of researchers, with notable advancements in color representation, texture, speed, and image quality. While previous studies focused on transferring artistic style across entire content images, a new approach proposes to transfer style specifically to objects within the content image based on the style image and maintain photorealism. Recent techniques have produced intriguing creative effects, but often only work with artificial effects, leaving real flaws visible in photographs used as references for styles. The suggested approach employs a two-dimensional wavelet transform (WT) to achieve style transfer by adjusting image structure with high-pass and low pass filters (LPF). Preserving the information content and numerical attributes of VGGNet19 through WT-based style transfer using the db5 WT at level 5, we can achieve a peak signal-to-noise ratio (PSNR) value of up to 96.76725. The qualitative result of the proposed methodology is compared with other existing algorithm. Also, the time complexity of the proposed methodology on different hardware platforms has been calculated and presented in the paper. The proposed methodology able to maintains appealing and precise quality of resultant image.
Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller Bouaddi, Abdessamade; Rabeh, Reda; Ferfra, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8186

Abstract

In this paper, a new hybrid optimization algorithm known as particle swarm optimization and grey wolf optimizer (PSO-GWO) based proportional integral derivative (PID) controller is suggested for automatic voltage regulator (AVR) system terminal tracking problem. The main objective of the suggested approach is to reduce crucial performance factors such as rise time, settling time, peak overshoot and peak time of the voltage of the power system in order to improve the AVR system's transient response. This analysis was compared to results obtained from existing heuristic algorithm-based approaches found in the literature, proving the improved PID controller's enhanced performance obtained through the suggested approach. Furthermore, the performance of the tuned controller with respect to disturbance rejection and its robustness to parametric uncertainties were evaluated separately and compared with existing control approaches. According to the obtained comparison results and from all simulations, using MATLAB-Simulink tool, it has been noted that the PID controller optimized using PSO-GWO algorithm has superior control performance compared to PID controllers tuned by ABC, DE, BBO and PSO algorithms. The main conclusion of the presented study highlights that the recommended strategy can be effectively implemented to improve the performance of the AVR system.
Glaucoma detection in retinal fundus images using residual network architecture Islami, Fajrul; Sumijan, Sumijan; Defit, Sarjon
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7621

Abstract

Glaucoma is a significant eye disease that can lead to irreversible vision loss if not detected and treated early. This research focuses on developing an automated glaucoma detection system using a combination of a convolutional neural network (CNN) with the residual network 18 (ResNet18) architecture, locality sensitive hashing (LSH), and Hamming distance calculation. The CNN model is trained to extract meaningful features from retinal images, while LSH enables efficient indexing and retrieval of similar images. Hamming distance calculations are utilized to measure the dissimilarity between binary codes obtained from LSH. A dataset of 506 retinal images, consisting of 117 glaucoma images, 19 glaucoma suspect images, and 370 healthy images. The proposed glaucoma detection system achieved an average accuracy of 99.96%, sensitivity of 99.97%, and specificity of 99.94% during training, and 82.37% accuracy, 86.78% sensitivity, and 73.55% specificity during testing. Comparative analysis demonstrated its superiority over traditional methods. Further research should focus on larger datasets and explore multi-class classification for different glaucoma stages. The proposed system has potential for early glaucoma detection, facilitating timely intervention, and preventing vision loss.
Design and analysis of sustainable photovoltaic solar charging system with battery storage for electric vehicles Radwan, Eyad; Awada, Emad; Nour, Mutasim
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7508

Abstract

This paper presents a sustainable electric vehicle (EV) charging system that operates in three modes of operation to maximize the yield of photovoltaic (PV) system. The design and analysis of the EV charging system is customized based on the operational or office hours of a corporation. The proposed system incorporates a battery pack capable of providing at least one day of autonomy to overcome the weather conditions during early morning, shading times, or cloudy days. In this study, the perturb and observe (PO) algorithm is modified and used to operate the PV system at maximum power point (MPP) when charging either the EV or the storage battery. The load current, in both cases, is regulated using proportional integral (PI) controllers and pulse width modulation (PWM) switching of DC-DC converter. The proposed system operation is switched between three modes (boost operation for direct charging of EV and discharging of storage battery, and buck operation for charging of storage battery) by a simple event-driven finite state machine (FSM). Simulation results showed excellent tracking behavior of the proposed system when supplying a 5 kW load with variation in solar irradiance between 1000 and 400 W/m2, battery state of charge (SOC) between 40% and 100%, and temperature between 15 to 39 ℃.
Ensemble neural networks with input optimization for flood forecasting Mohd Khairudin, Nazli; Mustapha, Norwati; Mohd Aris, Teh Noranis; Zolkepli, Maslina
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.6863

Abstract

Machine learning model has been widely used to provide flood forecasting including the ensemble model. This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. The aim is to highlight the improvement on accuracy of the forecast. Prior to the development of such ensemble model, data are optimized in two steps which are decomposed it using discrete wavelet transform (DWT) to reduce variations in the rainfall series and selecting dominant features using entropy called mutual information (MI) for the model. The result of the experiments indicates that ensemble neural networks model based on the data decomposition and entropy feature selection has outperformed individually executed forecast model in term of RMSE, MSE and NSE. This study proved that the proposed method has reduce the data variance and provide better forecasting with minimal error. With minimal forecast error the generalization of the model is improved.
Control system development for monitoring nutrition of curly mustard plants in horizontal NFT hydroponic based-IoT Rusdiyana, Liza; Suhariyanto, Suhariyanto; Sampurno, Bambang; Ardiyanti Pratama, Tania
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7920

Abstract

Agricultural technology with a hydroponic system provides an alternative for farmers and communities who have limited land. This research aims to make innovations with a hydroponic monitoring system that can be done remotely via the internet that combines 2 systems, namely horizontal technique and nutrient film technique (NFT). The sample used in this study was curly mustard seeds. To combine the 2 systems, researchers designed a hydroponic prototype system using internet of things (IoT) in the form of smart hydroponics in the Blynk application. This research uses literature studies for research reference and flowcharts to regulate the flow of the program to be researched. The results showed that by using the IoT and the Blynk application, owners can monitor the nutrient content and pH of curly mustard greens remotely. The system automatically controls nutrients and pH according to the desired settings. In the growth control system of mustard curly, the use of smart hydroponics is proven to be better. Harvestable plants at the age of 34 days. Unlike the conventional system, the harvest period is at the age of 40–45 days. Therefore, smart hydroponics is more efficient because it shortens the harvesting time and saves labor.
Enhancing spyware detection by utilizing decision trees with hyperparameter optimization Abualhaj, Mosleh M.; Al-Shamayleh, Ahmad Sami; Munther, Alhamza; Alkhatib, Sumaya Nabil; Hiari, Mohammad O.; Anbar, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7939

Abstract

In the realm of cybersecurity, spyware has emerged as a formidable adversary due to its persistent and stealthy nature. This study delves deeply into the multifaceted impact of spyware, meticulously examining its implications for individuals and organizations. This work introduces a systematic approach to spyware detection, leveraging decision trees (DT), a machine-learning classifier renowned for its analytical prowess. A pivotal aspect of this research involves the meticulous optimization of DT's hyperparameters, a critical operation for enhancing the precision of spyware threat identification. To evaluate the efficacy of the proposed methodology, the study employs the Obfuscated-MalMem2022 dataset, well-regarded for its comprehensive and detailed spyware-related data. The model is implemented using the Python programming language. Significantly, the findings of this study consistently demonstrate the superiority of the DT classifier over other methods. With an accuracy rate of 99.97%, the DT proves its exceptional effectiveness in detecting spyware, particularly in the face of more intricate threats. By advancing our understanding of spyware and providing a potent detection mechanism, this research equips cybersecurity professionals with a valuable tool to combat this persistent online menace.

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