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All Journal Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro
Hisham A. Shehadeh
Yarmouk University
Electrical & Electronics Engineering

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Motorcycle Parking Availability Monitoring Using YOLOv5 and Mobile-Based Systems R. Endro Wibisono; Anita Susanti; Kusuma Refa Haratama; Widi Aribowo; Karin Nur Fitria Ariyanti; Diego Oliva; Hisham A. Shehadeh; Abubakar Umar
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.
Power sharing based on starfish optimization algorithm in DC microgrid Widi Aribowo; Laith Abualigah; Diego Oliva; Abubakar Umar; Aliyu Sabo; Hisham A. Shehadeh
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.
Performance Evaluation of Sensor Data Filtering Methods for Signal Processing in TVET Learning Applications Farid Baskoro; Hisham A. Shehadeh; Hewa Majeed Zangana; Tri Wrahatnolo; Puput Wanarti Rusimamto; Fendi Achmad; Aristyawan Putra Nurdiansyah
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 4 (2026): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i4.16883

Abstract

Technical and Vocational Education and Training (TVET) learning requires sensor measurement data that are stable, accurate, and easy to interpret. Raw LiDAR sensor data often contain fluctuations that may interfere with the readability results. This study employed an experimental-comparative design by comparing Moving Average, Median Filter, Savitzky-Golay, Butterworth, and Simple Kalman Filter. The data acquisition system used a VL53L0X LiDAR sensor and ESP32 microcontroller. Data processing was conducted in MATLAB on 10,500 samples at a sampling frequency of 50 Hz. The evaluation was carried out based on error metrics, signal stability, noise reduction, and filter responsiveness. The raw data had a standard deviation of 111.26 and still showed fluctuations that required reduction. A Greenhouse–Geisser-corrected repeated-measures ANOVA showed a significant effect of filtering method on segment-level residual RMSE, F(1.10,44.92)=26.23, p<0.001, partial η2=0.390. Bonferroni-adjusted comparisons showed that Savitzky–Golay produced significantly lower residual RMSE than the other methods, indicating stronger preservation of the raw-signal pattern. The results showed that Savitzky–Golay achieved the best overall trade-off, with the lowest residual deviation, the highest estimated SNR of 32.154 dB, and good pattern preservation without excessive smoothing. Butterworth and Simple Kalman provided stronger fluctuation reduction, although Kalman introduced greater deviation and a 39-sample delay. Moving Average offered simple smoothing, whereas the Median Filter was more suitable for impulsive noise and outliers. This study contributes a comparative evaluation of filtering methods from both signal-processing and TVET pedagogical perspectives, supporting filter selection based on smoothness, readability, noise reduction, and responsiveness in signal processing.
Co-Authors Abubakar Umar Abubakar Umar Aliyu Sabo Anita Susanti Aristyawan Putra Nurdiansyah Diego Oliva Farid Baskoro Fendi Achmad Hewa Majeed Zangana Karin Nur Fitria Ariyanti Kusuma Refa Haratama Laith Abualigah Puput Wanarti Rusimamto R. Endro Wibisono Tri Wrahatnolo Widi Aribowo