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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
Linear algorithm for data retrieval performance optimization in self-encryption hybrid data centers M. Al Assaf, Maen; Qatawneh, Mohammad; AlRadhi, AlaaAldin
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.v14i3.9320

Abstract

Contemporary data centers implement hybrid storage systems that consist of layers from solid-state drives (SSDs) and hard disk drives (HDDs). Due to their high data retrieval speed, SSDs layer is used to store important data blocks that have features like high frequency of access. To boost their security level, many of such systems implement self-encryption algorithms like advanced encryption standard (AES), Blowfish, and triple data encryption standard (3DES) with different key sizes that vary in their complexity and their decryption latency whenever a block is requested for read. Frequently accessed data blocks with increased decryption latencies are better to be migrated to the SSDs layer to decrease their retrieval latency. In this paper, we introduce a linear complexity algorithm hybrid self-encryption storage data migration (HSESM) that migrates important data blocks that requires long decryption latencies from the HDDs layer to the SSDs one. Performance evaluation shows that HSESM data migration process can reduce data blocks read latencies in 13.71%-23.61% under worst-case scenarios.
Ensemble and deep learning via median method for learning disability classification P. J., Anu; Ranjith Singh, K.
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.v14i3.8639

Abstract

The study explores the classification of students with and without learning disabilities (LD) through machine learning techniques, utilizing a real dataset and implementing bootstrapping for data augmentation. Noteworthy findings reveal the Adam optimizer's superior performance among various optimizers, achieving a true positive rate (TPR) of 0.97 and a false positive rate (FPR) of 0.02, with high precision, recall, and f1-score values. Additionally, ensemble learning, employing the median method, combines models like Random-ForestClassifier and KerasClassifier, and BaggingClassifier with KerasClassifier, resulting in improved performance. However, the Median-Combined model, integrating AdaBoostClassifier and KerasClassifier, stands out with an accuracy of 99.6%, along with elevated precision, recall, and f1-score values. The comprehensive classification report showcases an overall FPR of 0.0 and TPR of 0.999, highlighting the enhanced performance of the combined model. The significance of this study lies in underscoring the power of fusion between ensemble learning and deep learning techniques, leveraging the median method. This combined model exhibits superior performance, excelling in accuracy, precision, recall, and overall classification effectiveness. The innovative approach of combining both ensemble and deep learning methods through the median method not only advances the understanding of learning disability classification but also emphasizes the practical importance of integrating diverse methodologies for enhanced model performance.
Event-driven integration of electronic medical records with blockchain and InterPlanetary file system Arissabarno, Cahyo; Sukaridhoto, Sritrusta; Winarno, Idris; Putri Nourma Budiarti, Rizqi
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.v14i3.9355

Abstract

The integrity, security, and accessibility of electronic medical record (EMR) are often compromised by traditional systems, which struggle to ensure data integrity, transparent audit trails, and secure long-term storage. This research addresses these challenges by integrating EMR with a private blockchain and InterPlanetary file system (IPFS) cluster, using change data capture (CDC) for real-time updates and integrate with existing EMR systems, avoiding the need for building new EMR software. Implemented in the OpenEMR framework, the system's performance is evaluated across various processes, including document uploading, sharing, access, deletion, and integrity verification. Testing with anonymized medical records in PDF formats ranging from 1 MB to 100 MB shows that uploading to IPFS takes 0.7 seconds per MB, blockchain transaction processing averages 4.2 seconds, CDC time is 1.1 seconds per MB, and OpenEMR uploads average 0.98 seconds per MB. These results demonstrate significant improvements in data security, integrity, and availability, following the CIA triad principles. The system provides a traceable and secure solution for EMR management.
Enhancing low-light pedestrian detection: convolutional neural network and YOLOv8 integration with automated dataset Rendi, Rendi; Fitrianah, Devi
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.v14i3.8903

Abstract

This research aims to enhance the you only look once (YOLO) model for pedestrian detection in environments with varying lighting conditions, particularly in low-light scenarios. The primary contribution of this work is the integration of a convolutional neural network (CNN)-based low-light enhancement model, which transforms dark images into brighter, more discernible ones. This enhanced dataset is subsequently used to train the YOLO model, allowing it to learn from both the original and transformed data distributions. Unlike traditional YOLO training approaches, this method generates more accurate data representations in challenging lighting environments, leading to improved detection outcomes. The novelty of this approach lies in its dual-stage training process, which integrates a CNNbased low-light enhancement model with YOLO’s detection capabilities. This combination not only enhances pedestrian detection but also has the potential for application in other domains, such as vehicle detection and surveillance, particularly in challenging lighting conditions. The automatic dataset collection pipeline provides an efficient way to gather diverse training data across various scenarios. The YOLOv8 model trained on the low-light enhanced dataset significantly outperformed the baseline model trained only on the original dataset, with precision increased by 9.8%, recall by 45.7%, mAP50 by 26.8%, and mAP50-95 by 41.0% when validated on dark images.
Enhancing fruit recognition with robotic automation and salp swarm optimization for random forest classification Chakravarthy Malineni, Sai; Mytheen Basari Kodi, Kaja; Sakkarai, Jeevitha; Nallasivan, Gomathinayagam; Geetha, Mani; Ananthan, Bhuvanesh
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.v14i3.8917

Abstract

In response to the growing demand for automation and labor-saving solutions in agriculture, there has been a noticeable lack of advancements in mechanization and robotics specifically tailored for fruit cultivation. To address this gap, this work introduces a novel method for fruit recognition and automating the harvesting process using robotic arms. This work employs a highly efficient and accurate model utilizing a single shot multibox detector (SSD) for detecting the precise fruit position. Once the fruit's position is identified, the angles of the robot arm's joints are calculated using inverse kinematics (IK). Finally, the optimal path planning is ensured by the salp swarm optimization (SSO) assisted random forest (RF) classification. This approach enables the precise management of robotic arms without any interference with either the fruits themselves or other robotic arms. Through meticulous consideration of these factors, our method ensures seamless operation in agricultural environments. Experimental validation demonstrates the effectiveness of these techniques in detecting apple fruits outdoors and subsequently automating their harvesting using robotic arms. This successful implementation underscores the potential for widespread application of our approach in enhancing efficiency and productivity in fruit cultivation.
A reliable unsupervised sensor data fusion method for fault detection in brushless direct current motors Babitha Nair, B; Madathil, Baburaj
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.v14i3.8860

Abstract

This paper introduces an efficient and reliable unsupervised method for detecting faults in a brushless direct current (BLDC) motor based on abnormality identification in sensor-acquired vibration and sound signals through multi resolution decompostion and analysis. The research utilizes the double-density dual-tree complex wavelet transform (DD-DT-CWT) to extract important features from vibration signals, and incorporates audio feature extraction for the sound signals. The captured signals are divided into overlapping segments to improve fault localization, and the features of each segment are organized in a coefficient matrix. Subsequently, singular value decomposition (SVD) is applied to the resulting coefficient matrix from the vibration and audio signals. To effectively monitor the motor’s condition, the singular values from both sets of sensor data are combined. Analysing the decay patterns of the singular values enables the identification of faults in the BLDC motor under test. By establishing a suitable threshold for the decay slope of the singular values, the proposed method can accurately and precisely identify and categorize various faults in BLDC motors. This early fault detection can prompt predictive maintenance to ensure the optimal performance, reduced downtime and longevity of BLDC motors.
Public health challenges in the Cuzco region: a decade of anemia in vulnerable populations applying data mining Rubio Paucar, Inoc; Andrade-Arenas, Laberiano
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.v14i3.8381

Abstract

The objective of the research is to carry out an exhaustive analysis of anemia in the province of Cusco using the Rapid Miner Studio tool that allows an analysis of the number of most concurrent cases in each district of the province of Cusco. Different sources of information were consulted to take as a reference the impact of the disease in different parts of the world. Likewise, information was introduced about how information technologies manifest positive responses in certain diseases around the world. The knowledge discovery in databases (KDD) methodology was used, which consists of several phases proposed in the project, such as data selection, data preprocessing, data mining and evaluation of results. Consequently, this research will help to recognize the most abundant cases in the districts of the province of Cusco. The results obtained were that 348 confirmed cases of anemia occurred in the district of Espinar, being the most affected district. Finally, it was concluded that in different provinces, not only in Cusco, there is a high prevalence of the disease due to factors associated with its treatment.
Strategies, characteristics, and research gaps for improving microservices coupling design Gintoro, Gintoro; Sunardi, Sunardi
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.v14i3.9099

Abstract

The popularity of microservices architecture (MSA) has been pushed by the demand for scalable, maintainable, and efficient applications in the fastchanging digital ecosystem. The objective of this study is to determine strategies for improving service coupling in MSA, analyze the circumstances in which these strategies are successful, and recommend areas of research that need further development for future enhancements. We employed a systematic literature review (SLR) and the seven research gap methodology developed by Müller-Bloch and Kranz to pinpoint 10 essential strategies, such as API gateway and domain-driven design (DDD). The results of our study indicate that the effectiveness of each technique is contingent upon specific design criteria for the microservices, such as the presence of separate read and write operations for command query responsibility segregation (CQRS). To further enhance these techniques, it is crucial to address the research gaps that have been highlighted, particularly the lack of empirical studies on long-term repercussions. This study offers theoretical insights and practical assistance on how to improve the connection between services, thereby enabling the development of more resilient and easily maintainable applications based on MSA.
Insights into peer-to-peer botnet dynamics: reviewing emulation testbeds and proposing a conceptual model Parthipan, Mithiiran; Laghari, Shams Ul Arfeen; Jaisan, Ashish; Baig, Amber; Ali, Muhammad Asim; Karuppayah, Shankar
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.v14i3.8654

Abstract

Peer-to-peer (P2P) botnets have emerged as a resilient cybercrime tool, utilizing decentralized architectures to evade detection and complicate takedown efforts. Existing botnet emulation testbeds often fall short in replicating the dynamic and large-scale environments that these botnets operate in, limiting their effectiveness in research and defense strategy development. This paper addresses these gaps by proposing a scalable, flexible emulation testbed for P2P botnets that integrates advanced virtualization and automation technologies. Our framework enables the accurate emulation of real-world botnet behaviors without relying on reverse engineering, offering researchers a secure and adaptable environment to test and validate botnet detection and mitigation strategies. The testbed’s dynamic scalability and robust configuration management streamline experimentation across diverse network topologies and botnet types. Our results show that this approach significantly enhances the ability to study P2P botnets in a controlled, reproducible setting, providing valuable insights for advancing cybersecurity defenses.
Advanced trajectory tracking control for wheeled mobile robots under actuator faults and slippage Luong Tran, Duc; Tien Dung Cao, Nguyen; Du Phan, Van; Tu Duong, Dinh; Phuong Ho, Sy
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.v14i3.9047

Abstract

Trajectory tracking control for wheeled mobile robots (WMRs) faces significant challenges in real-world applications due to actuator faults, longitudinal and lateral slippage. This study proposes an innovative dual-loop control structure combining adaptive sliding mode control (ASMC) and backstepping control (BC), supported by robust fault observers, to address these challenges. The dynamic loop employs ASMC to handle model uncertainties and disturbances, while the kinematic loop integrates BC with fault information provided by the observers, enabling real-time error compensation. Simulation results show that the proposed method significantly reduces tracking errors and improves stabilization time compared to traditional SMC and ASMC controllers. The system exhibits enhanced fault tolerance and disturbance rejection, maintaining stability under both normal and faulty conditions. The effectiveness of this approach is demonstrated through simulations and theoretical analysis, ensuring system stability using Lyapunov stability theory. The proposed method enhances robustness, adaptability, and stability of WMRs, contributing significantly to the field of mobile robotics under adverse conditions.

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