Identifikasi Pelanggaran Lalu Lintas Kendaraan Mobil terhadap Larangan Putar Balik menggunakan Algoritma You Only Look Once (YOLOv11)

Tiza Farhani Maulia (Univeritas Islam Sultan Agung)
Dedy Kurniadi (Universitas Islam Sultan Agung)
Ghufron Ghufron (Universitas Islam Sultan Agung)

Populer: Jurnal Penelitian Mahasiswa
Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa

The purpose of this study is to identify and analyze traffic violations involving cars that violate U-Turn prohibitions using the You Only Look Once (YOLOv11) algorithm. Illegal U-Turns in restricted areas are a primary cause of traffic congestion and accidents. This study develops an automated, real-time U-Turn violation monitoring system by integrating the YOLOv11n model, ByteTrack tracking algorithm, and Streamlit interface using 2-zone polygon logic. The dataset covers 7 vehicle classes evaluated on 1,072 validation images. Experimental results show that the YOLOv11n model achieves a mean Average Precision (mAP@50) of 66.5%, Precision of 68.4%, and Recall of 64.2%. With an inference speed of 3.2 ms per frame (169 FPS), the 2-zone logic integrated into Streamlit accurately detects illegal maneuvers, updates real-time metrics, and minimizes false alarms without frame lag. The proposed system demonstrates the potential of combining object detection, vehicle tracking, and zone-based logic for automated monitoring of U-Turn violations in restricted areas.