Optimalisasi Sistem Deteksi Rekomendasi Persimpangan Jalan dalam Kepadatan Lalu Lintas Menggunakan Algoritma YOLO v11
-
Aziz Nurdiansyah
*
Universitas Teknologi Yogyakarta, Sleman,
Indonesia
- Anna Dina Kalifia Universitas Teknologi Yogyakarta, Sleman, Indonesia (*) Corresponding Author
Abstract
The crucial problem facing modern urban areas is traffic congestion, which causes significant time, economic, and environmental losses. Manual identification of traffic density has proven to be inefficient and error-prone, especially at intersections with high real-time density fluctuations. This research aims to design and test a real-time vehicle detection system that is robust against environmental variability and capable of providing accurate predictions of density levels, contributing to the transformation of traffic management from reactive to predictive.As a solution, a traffic density detection and analysis system based on Deep Learning is proposed, utilizing an optimized YOLOv11 model, integrating Image Processing and a Neural Network. YOLOv11 is used to accurately detect and classify various types of vehicles from CCTV video footage, even in low-light conditions, and the results serve as input for the Adaptive Traffic Light Control Module based on the Density.Preliminary results from model training show very fast convergence, achieving a comprehensive accuracy (mAP@0.5) of 0.956 on the validation set in just 10 epochs. Although testing on new test data yielded an overall class mAP@0.5 of 0.631, the model demonstrated superior performance for detecting large vehicles, such as trucks (mAP@0.5 = 0.962) and cars (mAP@0.5 = 0.935). This system is expected to provide accurate traffic density information, enable adaptive traffic light settings, and ultimately contribute to intelligent traffic management systems.
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References
Anthony, Herman, & Yulianto, A. (2024). Pengembangan Sistem Pengenalan Plat Nomor Pendahuluan Tinjauan pustaka. Jurnal Ilmiah KOMPUTASI, 23, 571–578. https://doi.org/https://doi.org/10.32409/jikstik.23.4.3659
Daeli, N., Gustiana, Z., & Satria, A. (2024). Deteksi Citra Kepadatan Lalu Lintas Pada Malam Hari Menggunakan Convolutional Neural Network. CompTech: Jurnal Ilmu Komputer Dan Teknologi, 1(1), 1–11. https://doi.org/10.63854/comptech.v1i1.8
Deryabin, I. (2022). Traffic lights as a factor in the regulation of traffic noise. Transportation Research Procedia, 63, 436–440. https://doi.org/10.1016/j.trpro.2022.06.032
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
Homepage, J., & Alfath Zulkarnain, I. (2025). Optimasi Yolov11 Melalui Hyperparameter Tuning dan DataAugmentasi untuk Meningkatkan Akurasi DeteksiKendaraan pada Kondisi Malam Hari. 5, 1294–1303. https://doi.org/10.57152/malcom.v5i4.2250
Huizen, R. R. (2024). Optimalisasi Rekayasa Lalu Lintas Melalui Teknologi Deteksi Objek. Jurnal Sistem Dan Informatika (JSI), 18(2), 111–117. https://doi.org/10.30864/jsi.v18i2.605
Krisdianto, N., Kyandra Atharizqi, R., Nurhasanaah, W. T., Informatika, P., Sultan, U., & Tirtayasa, A. (2025). Pengembangan Sistem Pengenalan Objek Multi-Kelas Berbasis Segmentasi Citra dengan YOLOv11 dan Streamlit. IJInf: International Jurnal of Informatics, 5, 1–18.
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
Muttakin, A. R., & Haryanto, D. (2024). Sistem Penghitungan Kendaraan Otomatis Menggunakan Metode Object Detection Algoritma CNN. Jurnal Bufeer Informatika, 10(2), 11–18. https://doi.org/https://doi.org/10.25134/buffer.v10i2.238
Nugroho, A., & Cahyono, M. R. A. (2022). Implementasi Object Recognition Pada Rambu-Rambu Dan Lampu Lalu Lintas Dengan Raspberry Pi Dengan Algoritma Yolov5. Sebatik, 26(2), 549–556. https://doi.org/10.46984/sebatik.v26i2.2047
Ottom, M. A., & Al-Omari, A. (2023). An Adaptive Traffic Lights System using Machine Learning. International Arab Journal of Information Technology, 20(3), 407–418. https://doi.org/10.34028/iajit/20/3/13
Pingky, G. P. R., Mat Syai’in, Zindhu Maulana Ahmad Putra, Ahmad Putra, Ii Munadhif, & Imam Sutrisno. (2024). Skema Koordinasi Persimpangan Untuk Kelancaran Arus Lalu Lintas Dengan Metode Neural Network. Jurnal Elektronika Dan Otomasi Industri, 11(2), 396–408. https://doi.org/10.33795/elkolind.v11i2.5367
Pradana, A. I., Harsanto, H., & Wijiyanto, W. (2024). Deteksi Rambu Lalu Lintas Real-Time di Indonesia dengan Penerapan YOLOv11: Solusi Untuk Keamanan Berkendara. Jurnal Algoritma, 21(2), 145–155. https://doi.org/10.33364/algoritma/v.21-2.2106
Prathap, B. R., Kumar, K. P., Chowdary, C. R., & Hussain, J. (2022). AI-Based Yolo V4 Intelligent Traffic Light Control System. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(4), 53–61. https://doi.org/10.14313/jamris/4-2022/33
Putra, W. P. N., Pradana, A. I., & Nurchim, N. (2024). Implementasi Sistem Penghitungan Volume Kendaraan Menggunakan YOLOv8. Jurnal Fasilkom, 14(2), 443–450. https://doi.org/10.37859/jf.v14i2.7395
Shi, Y., Li, X., & Chen, M. (2023). SC-YOLO: A Object Detection Model for Small Traffic Signs. IEEE Access, 11(February), 11500–11510. https://doi.org/10.1109/ACCESS.2023.3241234
Suardika, B., Malkhamah, S., & Rizka Fahmi Amrozi, M. (2025). Pemanfaatan Artificial Intelligence Dalam Mengukur Kinerja Ruas Jalan Margonda Raya Kota Depok. Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety), 12(1), 1–11. https://doi.org/10.46447/ktj.v12i1.665
Surya, A., & Wahyuni, I. (2025). Deteksi Kepadatan Lalu Lintas Pada CCTV Publik Pemerintah Kota Malang Menggunakan YOLOv8. SPECTA Journal of Technology, 9(2), 136–149. https://doi.org/10.35718/specta.v9i2.8481367
Wahidin, F. R., Witanti, W., Ramadhan, E., Jenderal, U., Yani, A., Bandung, K., & Korespondensi, P. (2025). Pengontrolan Lampu Lalu Lintas Menggunakan Teknologi Deteksi Kendaraan Yolov4 Traffic Light Control Using Yolov4 Vehicle Detection. 12(5), 975–984. https://doi.org/https://doi.org/10.25126/jtiik.2025125
Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., & Guo, X. (2022). Real-Time Vehicle Detection Based on Improved YOLO v5. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912274
Zulkarnain, I. A., & Kusrini. (2025). Optimasi Yolov11 Melalui Hyperparameter Tuning dan Data Augmentasi untuk Meningkatkan Akurasi Deteksi Kendaraan pada Kondisi Malam Hari. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(October), 1294–1303. https://doi.org/https://doi.org/10.57152/malcom.v5i4.2250
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