This research predicts traffic density based on CCTV footage of Palembang city traffic. It uses the YOLOv9 model to detect cars and motorcycles, achieving a training performance with an mAP 0.5 of 84.4%. Furthermore, it compares traffic density using the Decision Tree and Random Forest algorithms on Monday, Wednesday, Friday, and Saturday during the morning, afternoon, and evening. The evaluati…