Prediksi Tingkat Kepadatan Kendaraan Menggunakan Metode YOLO Berbasis Image Processing pada Video Jalan Raya - Nitral Sejak Terang Waruwu; Yoannes Romando Sipayung (Jurnal Algoritma, 2026)
Traffic congestion in urban areas causes time losses, increased fuel consumption, and a decline in environmental quality. Therefore, a reliable visual data-based traffic monitoring system is needed. This study develops a system for detecting and analyzing traffic density and identifying peak hours by utilizing the You Only Look Once (YOLO) algorithm as a deep learning approach. YOLO is used to detect and count vehicles from highway video data, and the detection results are stored in a database for temporal analysis using historical vehicle volume data. This analysis aims to identify traffic density patterns and rush hour periods without applying a time-series-based temporal prediction model. The system's performance is evaluated using precision, recall, and mean Average Precision (mAP) metrics, while the rush hour identification results are validated through comparison with field observations. Test results show that YOLO is capable of accurately detecting vehicles and that the developed system can consistently identify periods of traffic density. The integration of YOLO-based vehicle detection with web-based temporal analysis is expected to support travel decision-making in urban environments.