Akurasi Pengujian Model Hasil Training menggunakan YOLOv4 untuk Pengenalan Kendaraan di Jalan Raya

Ahmad Fali Oklilas (Universitas Sriwijaya)
sukemi (jurusan sistem komputer fakultas ilmu komputer universitas sriwijaya)
Dinda Dwinta (Jurusan Sistem Komputer Fakultas Ilmu Komputer Universitas Sriwijaya)
Ghinadhia Shofi (Jurusan Sistem Komputer Fakultas Ilmu Komputer Universitas Sriwijaya)
Nanda Putri Mariza (Jurusan Sistem Komputer Fakultas Ilmu Komputer Universitas Sriwijaya)
Sri Arum Kinanti (Jurusan Sistem Komputer Fakultas Ilmu Komputer Universitas Sriwijaya)
Yulia Amanda Sari (Jurusan Sistem Komputer Fakultas Ilmu Komputer Universitas Sriwijaya)

Jupiter
Vol 15 No 1d (2023): Jupiter Edisi April 2023

Traffic congestion is currently the main problem that occurs in big cities in Indonesia.Traffic flow analysis is an important basis for urban planning. Management of IntelligentTransportation System (ITS) has become a necessity today to manage heavy traffic problems.Intelligent transportation systems using computer vision techniques are increasingly attractingattention for traffic density detection. This research uses the You Only Look Once (YOLO version4 object detection method for vehicle classification and detection to obtain an optimal model.Testing the YOLOv4 model results in a mean average precision (mAP) of 80.12%. In video testingto detect motorcycles and cars, the total vehicle accuracy is 70.6% and the vehicle confidencelevel is 78.7%.