Sistem Analisis Kinerja Lalu Lintas Berbasis Deep Learning dengan Arsitektur You Only Look Once (YOLO)

Andi Ahdan Amir; Sukman; Astri Delviana Lihara; Muhammad Nabil

doi:10.51454/decode.v6i1.1503

Authors

Andi Ahdan Amir Teknik Sipil Universitas Nahdlatul Ulama Sulawesi Tenggara
Sukman Teknik Sipil Universitas Nahdlatul Ulama Sulawesi Tenggara
Astri Delviana Lihara Teknik Sipil Universitas Nahdlatul Ulama Sulawesi Tenggara
Muhammad Nabil Teknik Informatika Universitas Nahdlatul Ulama Sulawesi Tenggara

DOI:

https://doi.org/10.51454/decode.v6i1.1503

Keywords:

Deep Learning, Kinerja Lalu Lintas, Level of Service, PKJI 2023, YOLO

Abstract

Tingginya tingkat kemacetan lalu lintas di perkotaan memerlukan solusi pemantauan dan analisis yang cepat dan akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem analisis kinerja lalu lintas otomatis yang mengintegrasikan model deteksi objek YOLOv8 dengan parameter standar Pedoman Kapasitas Jalan Indonesia (PKJI) 2023. Berbeda dengan penelitian sebelumnya yang umumnya berfokus pada kuantifikasi kendaraan, sistem ini secara otomatis mengonversi data deteksi visual menjadi metrik kinerja teknis. Hasil pengujian menunjukkan model YOLOv8s mencapai nilai mAP@0.5 rata-rata 0.948. Implementasi sistem pada dashboard interaktif menunjukkan akurasi perhitungan jumlah kendaraan sebesar 96% dibandingkan data manual, yang kemudian diolah menjadi indikator Derajat Kejenuhan (Degree of Saturation) dan Tingkat Pelayanan (Level of Service). Temuan ini membuktikan bahwa integrasi deep learning dengan regulasi rekayasa transportasi nasional dapat meningkatkan efisiensi pemantauan infrastruktur jalan secara signifikan.

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