Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah

Bambang Minto Basuki ; Ondang Fajrul Falach
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan I... 📅 2026 Vol 4, No 1 🔗 DOI: 10.61132/mars.v4i1.1402
Abstract
The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.
Keywords
SORT; Traffic Object Classification; Traffic Object Counting; Traffic Object Detection; YOLOv5
How to Cite

Bambang Minto Basuki & Ondang Fajrul Falach (2026). Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah. Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer, 4(1). https://doi.org/10.61132/mars.v4i1.1402

Bambang Minto Basuki; Ondang Fajrul Falach, "Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah," Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer, vol. 4, no. 1, 2026.

Bambang Minto Basuki; Ondang Fajrul Falach. "Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah." Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer, vol. 4, no. 1, 2026.

Bambang Minto Basuki; Ondang Fajrul Falach. "Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah." Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer 4, no. 1 (2026).

Bambang Minto Basuki & Ondang Fajrul Falach (2026) 'Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah', Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer, 4(1). doi: 10.61132/mars.v4i1.1402.

Bambang Minto Basuki; Ondang Fajrul Falach. Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah. Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer. 2026;4(1).

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