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All Journal Bulletin of Electrical Engineering and Informatics Martabe : Jurnal Pengabdian Kepada Masyarakat PeTeKa Buletin Ilmiah Sarjana Teknik Elektro Jurnal Ilmiah Wahana Pendidikan Education and Counseling Journal Journal of Education Technology Information Social Sciences and Health Indonesian Research Journal on Education Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Umar, Abubakar
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Power sharing based on starfish optimization algorithm in DC microgrid Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Umar, Abubakar; Sabo, Aliyu; A. Shehadeh, Hisham
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9784

Abstract

This paper presents a starfish optimization algorithm (SFOA) method for optimizing control parameters in DC microgrids. SFOA is a new metaheuristic inspired by biology to solve optimization problems, which simulates the behavior of starfish, including exploration, preying, and regeneration. SFOA consists of two main phases of exploration and exploitation. This paper evaluates the performance of SFAO on droop control of DC microgrids by comparing with walrus optimizer (WO) and grasshopper optimization algorithm (GOA). From the simulation, SFOA shows superior capability. Validation on DC microgrid control using integral of time-weighted absolute error (ITAE) and integral of time-weighted squared error (ITSE). Simulation results demonstrate that the proposed technique exhibits a superior ITAE relative to WO and GOA, which are 6.88% and 8%, respectively. The performance validation results demonstrate that the SFOA approach exhibits potential and effective performance. The proposed method on DC microgrid control has been successfully applied and shows promising performance. The proposed methodology is particularly suitable for renewable energy integration in isolated or resource-constrained regions.
Motorcycle Parking Availability Monitoring Using YOLOv5 and Mobile-Based Systems Wibisono, R. Endro; Susanti, Anita; Haratama, Kusuma Refa; Aribowo, Widi; Ariyanti, Karin Nur Fitria; Oliva, Diego; Shehadeh, Hisham A.; Umar, Abubakar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.16087

Abstract

The increasing number of motorcycles in developing countries has intensified parking management challenges, particularly in high-density environments with irregular vehicle arrangements. This study proposes a motorcycle parking availability detection system using the YOLOv5 object detection algorithm to address limitations of conventional parking methods. The research contribution is the development of a context-aware detection framework using a locally collected dataset and the evaluation of its performance under real-world parking conditions.The dataset consists of 1,200 images collected from campus parking areas and is divided into training, validation, and testing sets. The images were annotated into occupied and vacant classes and trained using YOLOv5 with 100 epochs. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP@0.5) on a held-out test set.The results show that the model achieves an F1-score of 0.57 and mAP@0.5 of 0.566, indicating moderate detection performance in dense and occluded environments. Although a precision of 1.00 is obtained at a confidence threshold of 0.978, this condition significantly reduces recall, highlighting a trade-off between detection accuracy and coverage. The confusion matrix and recall–confidence analysis reveal that errors are primarily caused by occlusion, shadow effects, and background interference. Compared to previous studies focusing on car parking detection, this system demonstrates comparable performance while addressing the unique complexity of motorcycle parking. However, the relatively small dataset size and environmental variability limit generalization.In conclusion, the proposed system provides a feasible initial approach for motorcycle parking detection, but further improvements in dataset diversity, annotation quality, and model robustness are required to achieve reliable large-scale deployment.
Co-Authors A. Shehadeh, Hisham Abiola, Ajikanle Abdulbasit Abualigah, Laith Adah, Miftahussa Agnestina, Tamia Agustini, Putri Alfiani Anita Susanti Ariyanti, Karin Nur Fitria Astuti Darmiyanti Bambang Ismaya Haratama, Kusuma Refa Ibrahim Ibrahim Mashuriyah, Mashuriyah Nugroho, Iqbal Adi Oliva, Diego Olugbenga, Lawal Abdulwahab Ore-Ofe, Ajayi Palupi, Tiara Dwi R. Endro Wibisono Razak, Farhan Imam Rohman, Roja Syahrul Sabo, Aliyu Shehadeh, Hisham A. Waluyo, Kasja Eki Widi Aribowo Yadi Fahmi Arifudin