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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 3: June 2025" : 75 Documents clear
Tri-level lung cancer classification via deep learning based GoogleNet with computed tomography images Rathinam, Vinoth; Arunagiri, Ramathilagam; Krishnasamy, Valarmathi; Rajendran, Sasireka
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.v14i3.9258

Abstract

Lung cancer (LC) is one of the most prevalent causes of cancer-related death worldwide. World Health Organization (WHO) classifies LC into two broad histological subtypes: non-small cell lung cancer (NSCLC) which is the cause of about 85% of cases and small cell lung cancer (SCLC) which makes up the remaining 15%. Several issues can influence LC detection including poor image quality, insufficient training data, low-quality image characteristics, and poor tumor localization. To overcome these challenges a novel TRI-level LC classification via deep learning-based GoogleNet with computed tomography (CT) images (TRI-LCNet) approach has been proposed for early-stage LC detection using CT images. Initially, the LC-input images CT are collected from openly accessible datasets. The lung CT images have been preprocessed using a Gaussian star filter (GaSF) to decrease noise, followed by feature extraction using GoogleNet. The extracted LC features are then given into a support vector machine (SVM) which is utilized as a classification tool to distinguish between different classes of LC cases. The TRI-LCNet approach performance was assessed by several metrics: specificity, accuracy, F1 score, and recall. The outcomes show that the suggested method obtains a higher accuracy range of 96.93% for the early identification of LC.
An efficient inductor-capacitor-inductor-capacitor compensation topology for wireless power transfer system Irshad, Talha; Nauman, Malik; Emeroylariffion Abas, Pg
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.v14i3.8728

Abstract

Wireless power transfer (WPT) systems provide a promising alternative for charging various applications, including electric vehicles (EVs), biomedical implants, smartphones, and network sensors. However, these systems often struggle to maintain high efficiency under varying loading and coupling conditions. This paper addresses these challenges by proposing a novel hybrid inductor-capacitor-inductor-capacitor (LC-LC) compensation topology. The proposed LC-LC topology is specifically designed to outperform conventional single-element compensation topologies, such as series-series (SS) and series-parallel (SP) configurations, by effectively reducing leakage inductance between coils. An analytical model of the LC-LC topology is developed and validated through simulations using Keysight advanced design system (ADS) software. The results demonstrate that the LC-LC topology not only achieves a peak efficiency of 99.6% under optimal conditions but also maintains superior performance compared to SS and SP topologies, with only a slight decrease to 93% efficiency observed at low load resistances. These findings highlight the potential of the LC-LC topology to significantly enhance WPT system efficiency across a range of operating conditions.
Predicting the intention to adopt e-zakat payment services: a machine learning approach Abdul Samad, Nor Hafiza; Abdul Rahman, Rahayu; Masrom, Suraya; Omar, Norliana; Che Hasan, Haslinawati
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.v14i3.8512

Abstract

The technology evolution in the zakat collection and payment services has brought about a profound transformation in the global processes of gathering and distributing charitable contributions. Despite witnessing a positive trend in annual zakat collection in Malaysia, it has yet to reach its optimal level. Therefore, predictions regarding performance and comparisons across multiple models for online zakat collection hold crucial significance in improving the overall collection rate. This paper, utilizing data from 230 zakat payers, presents an empirical assessment of various machine learning algorithms aimed at predicting zakat payer intentions when utilizing online platforms for zakat payments. Additionally, this paper presents the analysis of machine learning features importance to justify the effect of technology acceptance model (TAM) and theory of technology readiness (TR) attributes in the machine learning algorithms for predicting e-zakat payment service adoption intention. The findings show that many of the machine learning models are able to perform for highly accurate results, with most achieving over 80% accuracy. The most crucial attribute influencing these predictions was found to be the TAM. This study's methodology is designed to be easily replicable, allowing for further detailed exploration of both the influencing factors and the machine learning algorithms used.
Unmasking effects of feature selection and SMOTE-Tomek in tree-based random forest for scorch occurrence detection Dumebi Okpor, Margaret; Eluemnor Anazia, Kizito; Adigwe, Wilfred; Abugor Okpako, Ejaita; Moses Setiadi, De Rosal Ignatius; Adimabua Ojugo, Arnold; Omoruwou, Felix; Erhovwo Ako, Rita; Ochuko Geteloma, Victor; Valentine Ugbotu, Eferhire; Chukwudi Aghaunor, Tabitha; Enadona Oweimeito, Amanda
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.v14i3.8901

Abstract

Scorch occurrence during the production of flexible polyurethane foam has been a menace that consistently, jeopardize a foam’s integrity and resilience. It leads to foam suppression and compactness integrity failure due to scorch. There is always the increased likelihood of scorching, and makes crucial the utilization of methods that seek to avert it. Studies predict that the formation of foam constituent processes via optimization using machine learning have adequately trained models to effectively identify scorch occurrence during the profiling in the polyurethane foam production. Our study utilizes the random forest (RF) ensemble with feature selection (FS) and data balancing technique to identify production predictors. Study yields accuracy of 0.9998 with F1-score of 0.9819. Model yields 2-distinct cases for (non)-occurrence of scorch respectively, and the ensemble demonstrates that it can effectively and efficiently predict the occurrence of scorch in the production of flexible polyurethane foam manufacturing process.
Structure of 6-dimensional finite non-commutative algebras with many single-sided units Thu Duong, May; Andreevich Moldovyan, Alexander; Andreevich Moldovyan, Nikolay; Hieu Nguyen, Minh; Thi Do, Bac
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.v14i3.9064

Abstract

Finite Associative Noncommutative Algebras (FANAs) have gained considerable attention as a key foundational element for post-quantum (PQ) public-key (PK) cryptosystems, particularly those with a hidden group. These systems exploit the complexity of the hidden discrete logarithm problem (HDLP) and the challenge of solving large system of power equations. The structure of 6-dimensional FANAs over the finite field GF(p), which can include global single-sided units in different configurations (p2, p3, and p4), plays an essential role in assessing the security of these cryptosystems. A novel PQ signature algorithm has been proposed based on FANAs with p2 global single-sided units, while the others have been deemed less suitable for supporting the proposed algorithm. The decomposition of these algebras into isomorphic subalgebras, each with a global two-sided unit, significantly contributes to understanding the design of PQ cryptosystems that use FANAs with a large number of global singlesided units as their algebraic framework. 
Integrating low-cost vision for autonomous tracking in assistive robots Martínez, Fredy; Martínez, Fernando; Penagos, Cristian
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.v14i3.9242

Abstract

This study presents the implementation of a real-time tracking system for the ARMOS TurtleBot, a robot designed for assistive applications in domestic environments. The system integrates two OmniVision 7670 (OV7670) camera modules positioned 7 cm apart to emulate human-like stereoscopic vision, enabling depth perception and three-dimensional object tracking. An embedded system platform 32-bit (ESP32) microcontroller captures and processes images from both cameras, calculates disparities, and transmits data to a Raspberry Pi via WebSockets. The Raspberry Pi, equipped with robot operating system (ROS), performs further analysis using open computer vision (OpenCV) and visualizes results in real-time with ROS visualization (RViz), allowing the robot to autonomously track moving objects such as humans or pets. Key optimizations, including image resolution reduction and data filtering, were implemented to enhance processing efficiency within the hardware constraints. The proposed approach demonstrates the feasibility of low-cost, real-time object tracking in assistive robotics, highlighting its potential for applications that require humanrobot interaction in dynamic indoor settings. This work contributes to the field by providing a practical solution for integrating stereoscopic vision and real-time decision-making capabilities into small-scale robots, promoting further research and development in affordable robotic assistance systems.
Enhancing realism in handwritten text images with generative adversarial networks Dubey, Parul; Nayak, Manjushree; Gehani, Hitesh; Kukade, Ashwini; Keswani, Vinay; Dubey, Pushkar
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.v14i3.9190

Abstract

Image synthesis is particularly important for applications that want to create realistic handwritten documents, which is why handwritten text generation is a critical area within its domain. Even with today's highly advanced technology, generating diverse and accurate representations of human handwriting is still a tough problem because of the variability in style. In this study, we tackle the problem of instability during the training phase of generative adversarial networks (GANs) for generating handwritten text images. Using the MNIST dataset, which includes 60,000 training and 10,000 test images of handwritten digits, we trained a GAN model to generate synthetic handwritten images. The methodology involves optimizing both the generator and discriminator using adversarial training, binary cross-entropy loss, as well as the optimizer Adam. A brand-new decaying learning rate schedule was introduced to speed up convergence. Performance was evaluated using the Fréchet inception distance (FID) metric. The results show that this model effectively generated high-quality synthetic images of handwritten digits, which resembled real data closely in the face of it all and also that there was a steady reduction in FID scores across epochs indicating improved performance.
Influences of impulse generators on the impulse characteristics of grounding systems Muhammad, Usman; Aman, Fazlul; Mohamad Nor, Normiza; Nadia Ahmad, Nurul; Osman, Miszaina
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.v14i3.8467

Abstract

It is important to ensure the effectiveness of the experimental test set up and to accurately characterize grounding systems under high impulse conditions, the study on the effect of impulse generator is therefore needed. As with other experimental work, the test results may be influenced not only by the characteristics of the test load under study, but also the test arrangement, rating of the impulse generator and transducers. In this work, sources of this overshoot/spike observed in voltage and current traces of 1-rod, 3-rod, and 4-rod electrodes subjected to two impulse current generators of different rating: generating at maximum voltage and current of 100 kV, 1.5 kA, and 300 kV, 10 kA with the same response time of 1.2/50 μs are identified with the aid of simulation work.
The adoption of online food delivery in facing COVID-19 among the Indonesian food MSMEs Yasirandi, Rahmat; Thanasopon, Bundit
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.v14i3.8712

Abstract

This study investigates the factors influencing the Indonesian food micro, small, and medium enterprises (MSMEs) in adopting online food delivery (OFD) during the corona virus disease-2019 (COVID-19) pandemic, by employing the technology-organization-environment (TOE) framework. Through a quantitative approach involving 378 respondents, this research explores the multi-dimensional factors affecting OFD service adoption, there are innovation compatibility, innovation complexity (IC), innovation cost, owner’s self-efficacy, owner’s commitment, customer pressure (CSP), competitive pressure (CMP), government support (GS), and health protocol guarantee. Employing covariance-based structural equation modeling (CBSEM), the study reveals interesting relationships among the proposed factors. The findings underscore the role of GS and health protocol guarantees in enhancing owner's self-efficacy and commitment towards OFD adoption. Moreover, it challenges the presumed barriers of IC, suggesting a nuanced understanding of adoption process amid a crisis. This study not only enriches the theoretical discourse on technology adoption in the context of a pandemic but also provides practical implications for stakeholders in navigating the post-pandemic business landscape. Future research directions are proposed to explore the continuous intention of food MSMEs towards OFD services postpandemic, highlighting the evolving nature of the global business environment and the enduring impact of the COVID-19 pandemic on food industries.
Energy and path loss analysis of wireless sensor networks on a robotic body (WSRobotic) Z. Iskandarani, Mahmoud
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.v14i3.9354

Abstract

The objective of this work is to simulate and mathematically model both path loss and transmitted energy in a robotic wireless sensor network (WSN). The simulation and analysis showed an increase in both path loss and transmitted energy as a function of distance. The correlation between transmitted energy and path loss proved to be exponential relationship with both logarithmic and power relationships between path loss and distance. Both expressions describing path loss, using close-in (CI) dual model and transmitted energy, using wireless body area network (WBAN) model, are modified and combined in one single expression to enable optimization of energy management. The newly developed expression is simulated and produced reliable results, relating effect of frequency and message size on transmitted energy as a function of distance. Combining these results with the results showing effect on path loss on transmitted energy, enables a better optimization of energy management of nodes on robotic body. The main objective of this work, which is the development of a single expression relating transmitted energy to critical parameters (frequency, path loss exponent, message size, distance) is achieved and is logically derived and based on analysis using two separate models for path loss and transmitted energy.

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