Perancangan dan Analisis Kinerja Sistem Penghitung Lalu Lintas Otomatis Berbasis YOLOv8
DOI:
https://doi.org/10.61132/mars.v3i6.1311Keywords:
Computer Vision, Congestion, Density, Traffic Detection, YOLOv8Abstract
Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.
References
Aini, Q., Lutfiani, N., Kusumah, H., & Zahran, M. S. (2021). Deteksi dan pengenalan objek dengan model machine learning: Model YOLO. CESS (Journal of Computer Engineering, System and Science), 6(2), 192–198.
Ardiansyah, A., Triloka, J., & Kurniawan, K. (2024). Evaluasi kinerja model YOLOv8 dalam deteksi kesegaran buah. Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER), 16, 357–368.
Astiadewi, E., Dikananda, A. R., & Rohman, D. (2025). Algoritma YOLOv8 untuk meningkatkan analisis gambar dalam mendeteksi jerawat. Jurnal Informatika Teknologi dan Sains, 7(1), 346–353. https://doi.org/10.51401/jinteks.v7i1.5432
Bakirci, M. (2024). Real-time vehicle detection using YOLOv8-nano for intelligent transportation systems. Traitement du Signal, 41(4), 1727–1740. https://doi.org/10.18280/ts.410407
Butilă, E. V., & Boboc, R. G. (2022). Urban traffic monitoring and analysis using unmanned aerial vehicles (UAVs): A systematic literature review. Remote Sensing, 14(3), Article 620. https://doi.org/10.3390/rs14030620
Fauzi, A. A., Wahid, A., & Kaswar, A. B. (2025). Pengembangan sistem deteksi kecepatan kendaraan overspeed dengan YOLOv8 di area jalan tol. JIMU: Jurnal Ilmiah Multidisipliner, 3(2), 889–910.
Hayati, N. J., Singasatia, D., & Muttaqin, M. R. (2023). Object tracking menggunakan algoritma You Only Look Once (YOLO)v8 untuk menghitung kendaraan. Komputa: Jurnal Ilmiah Komputer dan Informatika, 12(2), 91–99. https://doi.org/10.34010/komputa.v12i2.10654
Hidayattullah, R., Suarna, N., Ali, I., & Efendi, D. I. (2025). Deep learning algoritma YOLOv8 untuk meningkatkan analisis kepadatan lalu lintas. Jurnal Informatika dan Teknik Elektro Terapan, 13(2), 1–8. https://doi.org/10.23960/jitet.v13i2.5749
Marcelleno, D. J., & Putra, M. P. K. (2025). Performance evaluation of YOLOv8 in real-time vehicle detection in various environmental conditions. Jurnal Teknik Informatika (JUTIF), 6(1), 269–279. https://doi.org/10.52436/1.jutif.2025.6.1.3916
Putra, R. R., Maimunah, M., & Sasongko, D. (2024). Implementasi algoritma YOLOv8 (You Only Look Once) dalam deteksi penyakit daun durian. Building of Informatics, Technology and Science (BITS), 6(3), 1–9. https://doi.org/10.47065/bits.v6i3.6136
Rasjid, A. A., Rahmat, B., & Sihananto, A. N. (2024). Implementasi YOLOv8 pada robot deteksi objek. Journal of Technology and System Information, 1(3), Article 9. https://doi.org/10.47134/jtsi.v1i3.2969
Subekti, J. N., & Putra, A. D. (2025). Deteksi traffic pada jumlah kendaraan yang lewat di You See Indonesia menggunakan metode YOLOv8. Information System Journal, 7(2), 78–86. https://doi.org/10.24076/infosjournal.2024v7i02.1736
Taufiqurrahman, T., Hadi, A. P., & Siregar, R. E. (2024). Evaluasi performa YOLOv8 dalam deteksi objek di depan kendaraan dengan variasi kondisi lingkungan. Jurnal Minfo Polgan, 13(2), 1755–1773. https://doi.org/10.33395/jmp.v13i2.14228
Therino Elevan, R., Hermanto, D., & Widiyanto, E. P. (2025). Deteksi jumlah kendaraan roda empat menggunakan YOLO. Integrative Perspectives of Social and Science Journal, 2(1), 1647–1654.
Wijanarko, R. G., Pradana, A. I., & Hartanti, D. (2024). Implementasi deteksi drone menggunakan YOLO (You Only Look Once). Jurnal FASILKOM, 14(2), 437–442.
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