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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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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.
Design and performance evaluation of a high-efficiency circular microstrip patch antenna for RFID applications at 900 MHz Sahel, Zahra; Habibi, Sanae; Bendali, Abdelhak; ALtalqi, Fatehi; Mouhib, Omar; Habibi, Mohamed
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.v14i3.9110

Abstract

This study presents a high-efficiency circular microstrip patch antenna designed for radio frequency identification (RFID) applications simulation results illustrate the performance of a circular microstrip patch antenna operating at 900 MHz. Microstrip antennas are renowned for their ability to meet the requirements of compact, lightweight designs, ensuring compatibility, and ease of integration. This research focuses on the development of a circular microstrip antenna, formed as a circular patch on a 0.035 mm thick FR-4 substrate. The design was realized using a substrate with a relative permittivity (εr) of 4.3, a loss tangent (tan δ) of 0.021 and a substrate height (h) of 1.6 mm. The antenna dimensions are small, measuring 58×45 mm, with a circular patch radius of 17 mm. The antenna operates over a frequency range from 0.5 GHz to 2 GHz. Key performance parameters include a return loss of -49.8 dB, a wide bandwidth of 150 MHz, a voltage standing wave ratio (VSWR) of 1.009, a gain of 2.161 dB, and a directivity of 2.200 dBi. Antenna design and simulation were carried out using computer simulation technology (CST) Studio Suite Software, specifically adapted to RFID applications.
Driving behavior analytics: an intelligent system based on machine learning and data mining techniques Arabiat, Areen; Altayeb, Muneera
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.v14i3.9095

Abstract

One of the most common causes of road accidents is driver behavior. To reduce abnormal driver behavior, it must be detected early on. Previous research has demonstrated that behavioral and physiological indicators affect drivers' performance. The goal of this study is to consider the feasibility of classifying driver behavior as either aggressive (sudden left or right turns, accelerating and braking), normal (average driving events) or slow (keeping a lower-than-average speed). Innovation in data mining and machine learning (ML) has allowed for the creation of powerful prediction tools. ML techniques have shown potential in predicting driver behavior, with classification being a critical study area. The data set was gathered using the Kaggle platform. This study classifies driver behavior using Orange3 data mining tools and tests several classifiers, including AdaBoost, CN2 rule inducer, and random forest (RF) classifiers. The results showed that AdaBoost was superior in predicting driver behavior, with 100% accuracy, while the classification accuracy in CN2 rule inducer and RF was 99.8% and 95.4%, respectively. These results demonstrate the possibility of early and highly accurate driver behavior prediction and use it to create a ML-based driver behavior detection system.
Deep residual bidirectional long short-term memory fusion: achieving superior accuracy in facial emotion recognition Munsarif, Muhammad; Ku-Mahamud, Ku Ruhana
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.v14i3.9090

Abstract

Facial emotion recognition (FER) is a crucial task in human communication. Various face emotion recognition models were introduced but often struggle with generalization across different datasets and handling subtle variations in expressions. This study aims to develop the deep residual bidirectional long short-term memory (Bi-LSTM) fusion method to improve FER accuracy. This method combines the strengths of convolutional neural networks (CNN) for spatial feature extraction and Bi-LSTM for capturing temporal dynamics, using residual layers to address the vanishing gradient problem. Testing was performed on three face emotion datasets, and a comparison was made with seventeen models. The results show perfect accuracy on the extended Cohn-Kanade (CK+) and the real-world affective faces database (RAF-DB) datasets and almost perfect accuracy on the face expression recognition plus (FERPlus) dataset. However, the receiver operating characteristic (ROC) curve for the CK+ dataset shows some inconsistencies, indicating potential overfitting. In contrast, the ROC curves for the RAF-DB and FERPlus datasets are consistent with the high accuracy achieved. The proposed method has proven highly efficient and reliable in classifying various facial expressions, making it a robust solution for FER applications.
Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning Umamaheswari, Umamaheswari; Kannan, Kannan; Rozario, Juliet; Manimekala, Manimekala
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.v14i3.9400

Abstract

The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bioinformatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification.

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