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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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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.
Comparative analysis of PoS and PoA consensus in Ethereum environment for blockchain based academic transcript systems Wicaksono, Palguno; Hatta, Puspanda; Aristyagama, Yusfia Hafid
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.v14i3.9219

Abstract

Many educational institutions worldwide now use blockchain to verify electronic document, often relying on Ethereum 1.0, which uses proof of work (PoW) or proof of authority (PoA). However, Ethereum 2.0, launched in 2022 by Ethereum Foundation operates on proof of stake (PoS). This study provides comparative analysis of PoS and PoA consensus in Ethereum environment specifically focusing on performance and scalability in the context of academic transcript databases. To demonstrate this, a student academic reputation information system was developed using two different blockchain technologies: Ethereum 1.0 with PoA and Ethereum 2.0 with PoS. This setup was used to obtain comparative analysis data for the two blockchain systems by measuring the throughput and latency. We observed how these platforms responded to an increasing number and frequency of transactions with Hyperledger Caliper. Results indicates that in performance testing, both consensus mechanisms exhibited. Scalability tests revealed that both consensus mechanisms experienced increased latency with higher loads. However, PoA system was superior in average throughput and latency than PoS system except in high transaction of data addition. The experiment result show that PoA system better than PoS system in context of academic transcript databases, making it more suitable to be implemented on that context.
Breast cancer detection and classification using deep learning techniques based on ultrasound image Mohammed Khalaf, Abdulqader; Abdel Razek, Mohammed; El-Dosuky, Mohamed; Sobhi, Ahmed
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.v14i3.8397

Abstract

Breast cancer ranks as the most prevalent form of cancer diagnosed in women. Diagnosis faces several challenges, such as changes in the size, shape, and appearance of the breast, dense breast tissue, and lumps or thickening, especially if present in only one breast. The major challenge in the deep learning (DL) diagnosis of breast cancer is its non-uniform shape, size, and position, particularly with malignant tumors. Researchers strive through computer-aided diagnosis (CAD) systems and other methods to assist in detecting and classifying tumor types. This work proposes a DL system for analyzing medical images that improves the accuracy of breast cancer detection and classification from ultrasound (US) images. It reaches an accuracy of 99.29%, exceeding previous work. First, image processing is applied to enhance the quality of input images. Second, image segmentation is performed using the U-Net architecture. Third, many features are extracted using Mobilenet. Finally, classification is performed using visual geometry group 16 (VGG16). The accuracy of detection and classification using the proposed system was evaluated.
Determination analysis of main dimensions of induction motors for railway propulsion system Kamar, Syamsul; Lestari, Meiyanne; Luthfiyah, Hilda; Adam Qowiy, Okghi; Syamsuddin Hasrito, Eko; Hidayat, Sofwan
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.v14i3.8554

Abstract

Induction motors are used in industrial production processes. As for its use as a traction motor, it requires special design and manufacture. The type of induction motor that is widely chosen as a traction motor for railways is a squirrel-cage three-phase induction motor. The main consideration for the selection or design of an induction motor as a railway traction motor is the torque requirement to drive the train. Other parameters that are considered in the selection of an induction motor as a traction motor include available spaces for installation. This research is using a three-phase, 2,300 VAC, 480 kW, and 50 Hz induction motor. By using the application program for determining the parameters of the induction motor, it shows that the motor produces a moderate output coefficient (between maximum and minimum) and produces a torque greater than induction motor torque in general. As a result of the analysis, this induction motor is suitable to be used as a motor for the railway, where greater torque is required.
Enhancing 3D building visualization and real-time monitoring in construction through IFC and IoT integration Surya Kumara, I Made; Made Ngurah Desnanjaya, I Gusti; Nataraj, Kannan
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.v14i3.9263

Abstract

The integration of industry foundation classes (IFC) and internet of thing (IoT) addresses a key challenge in construction: real-time data visualization on specific building storeys. Traditional methods often struggle with data integration and timely monitoring. This study introduces a web-based platform that combines three-dimensional (3D) technology, IFC models, and IoT sensors to enhance visualization and monitoring in construction projects. Unlike prior approaches that focus on static visualization or lack real-time IoT integration, this platform delivers dynamic, storey -specific updates, enabling real-time monitoring of critical building parameters. A case study showed that file size significantly impacted loading speed, ranging from 0.17 kB/ms (97.3 kB model in 572 ms) to 11.72 kB/ms (7.2 MB model in 629 ms). Despite a slight drop in frame rate from 60 to 55 frames per second (FPS), the system maintained smooth user interactions. Memory usage increased from 180 MB to 314 MB to handle complex 3D models and IoT data in real time. These findings demonstrate that integrating IFC with IoT enhances data visualization, providing more efficient decision-making tools for construction stakeholders and improving on-site coordination and resource management.

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