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All Journal Pena Teknik : Jurnal Ilmiah Ilmu-ilmu Teknik Decode: Jurnal Pendidikan Teknologi Informasi
Lihara, Astri Delviana
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Transportation Other

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Optimization of Traffic Performance Analysis in Kendari City with Deep Learning Amir, Andi Ahdan; Sukman, Sukman; Lihara, Astri Delviana; Muhammad Nabil , Muhammad Nabil; Duwi Nurmayanti , Duwi Nurmayanti
PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik VOLUME 11 NUMBER 1 MARCH 2026
Publisher : Faculty of Engineering, Andi Djemma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51557/3fxt8a88

Abstract

Urban traffic congestion in developing cities like Kendari requires rapid and accurate monitoring solutions. This research aims to design and implement an integrated traffic performance analysis system utilizing the YOLOv8 deep learning architecture. The system, named KILALIN, automates vehicle detection, classification, and tracking to calculate road capacity and saturation levels based on the PKJI 2023 standards. A comprehensive dataset of 1,606 annotated images was utilized, partitioned into training (57%), validation (29%), and testing (13.7%) subsets. The developed YOLOv8s model achieved high performance with a mean Average Precision (mAP@0.5) of 0.948, precision of 0.941, and recall of 0.935 across all vehicle classes. Functional validation through black-box testing confirmed the system's ability to process real-time video inputs under various conditions. Comparative results with manual surveys showed a 96% counting accuracy, indicating the system's robustness in quantified traffic flow estimation. Furthermore, the integration of automatic Passenger Car Equivalent (EMP) conversion allows for an immediate determination of the Degree of Saturation (DS) and Level of Service (LoS). These findings indicate that the YOLO-based traffic performance analysis system provides a reliable and efficient framework for urban traffic management, effectively replacing conventional manual survey methods while maintaining high technical standards.
Sistem Analisis Kinerja Lalu Lintas Berbasis Deep Learning dengan Arsitektur You Only Look Once (YOLO) Amir, Andi Ahdan; Sukman; Lihara, Astri Delviana; Nabil, Muhammad
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1503

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.
Co-Authors Amir, Andi Ahdan Duwi Nurmayanti , Duwi Nurmayanti Muhammad Nabil Muhammad Nabil , Muhammad Nabil Sukman Sukman, Sukman