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All Journal Jurnal Pengabdian Masyarakat Bangsa International Journal of Applied Mathematics and Computing.
Abil, Muhammad
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Penguatan Jaringan Usaha Kecil Dan Menengah (UKM) Pariwisata: Mendukung Ekonomi Lokal Melalui Kerja Sama Bisnis Faretra, Karen; Wahyudi, Jefri; Laila, Mei Ratna; Abil, Muhammad; Sari, Marta Widian
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 5 (2024): Juli
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i5.1070

Abstract

Tujuan pengabdian ini adalah memberikan Pemahaman dan pengetahuan pelaku usaha sewa mobil-mobil listrik di area sekitar pantai padang mengenai penerapan strategi pengembangan kemampuan dalam meningkatkan promosi usaha serta pendapatan. dalam pengabdian ini kami menggunakan analisis secara detail dengan pendekatan sosialisasi, observasi dan wawancara. Hasil kegiatan menunjukkan bahwa usaha sewa mobil listrik mainan mengalami beberapa keluhan dan masalah dari para pelanggan yang hendak menyewa mobil listrik di sekitar area pantai padang. Untuk itu kami memberikan edukasi kepada pelaku usaha mengenai usaha yang mereka jalankan dan bagaimana meningkatkan pelayanan agar pelanggan merasa senang dan mendapatkan loyalitas pelanggan,. Dengan adanya pengabdian ini diharapkan bisa memperoleh pengalaman praktis di dunia usaha serta dapat melakukan pengkajian terhadap penerapan digital marketing untuk dimasa yang akan mendatang.
Comparative Evaluation of YOLOv5, YOLOv7, and YOLOv8 for Outdoor Traffic Object Detection Achmad, Refi Riduan; Abil, Muhammad; Fadhilah, Muhammad Raihan; Sandi
International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i2.291

Abstract

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.
Co-Authors Achmad, Refi Riduan Fadhilah, Muhammad Raihan Faretra, Karen Laila, Mei Ratna Sandi Sari, Marta Widian Wahyudi, Jefri