PENGEMBANGAN SISTEM PERHITUNGAN JUMLAH KENDARAAN BERDASARKAN JENIS KENDARAAN MENGGUNAKAN ALGORITMA YOLO SECARA REALTIME

Adri Surya Kusuma; Afu Ichsan Pradana; Bondan Wahyu Pamekas

doi:10.36080/skanika.v7i2.3201

Adri Surya Kusuma Universitas Duta Bangsa Surakarta
Afu Ichsan Pradana Universitas Duta Bangsa Surakarta
Bondan Wahyu Pamekas Universitas Duta Bangsa Surakarta

DOI: https://doi.org/10.36080/skanika.v7i2.3201

Keywords: YOLOv9, Vehicle Detection, Confusion Matrix, Real-time Computer Vision

Abstract

This study developed a vehicle counting system based on vehicle types using the YOLO algorithm in real-time. With the advancement of artificial intelligence technology, particularly in computer vision, outdoor media has also advanced through the computation of CCTV image results installed on such media. The significance of this research is found in its ability to provide accurate and real-time traffic data that can be utilized by local governments, traffic management companies, property developers, and advertising companies for traffic planning, security, and marketing analysis. The research method involved training the YOLOv9 model and centroid-based tracking on a dataset that included four vehicle classes: car, motorcycle, bus, and truck. The results showed that the developed system could detect and track vehicles with high accuracy, achieving a mAP of 0.9, precision of 0.909, recall of 0.828, and an F1-Score of 0.867. However, the system's performance heavily depends on the hardware specifications used, and the detection for motorcycles has lower evaluation scores compared to other vehicle types. This study indicates that improving hardware specifications and optimizing the model can enhance system performance. These findings are important as they reveal the considerable potential of using the YOLO algorithm in real-time traffic monitoring applications.

Downloads

Download data is not yet available.

References

[1] B. Prakoso, R. Himmah, and F. K. Illahi, “Dinamika Politik Menuju Pemilihan Presiden 2024 di Indonesia: Studi Social Network Analysis,” J. Lanskap Politik, vol. 1, no. 3, p. 107, 2023.
[2] M. Badri, “Perancangan Media Luar Ruang untuk Kampanye Pencegahan Covid-19 di Komunitas Perdesaan,” Menara Riau, vol. 14, no. 2, p. 68, 2021.
[3] B. Lavega Adiputra, P. Christin Harnita, P. Hergianansari, and I. Komunikasi Universitas Satya Kencana, “Penggunaan Media Baliho oleh Puan Maharani Menjelang Pemilu Indonesia 2024 di Era 5.0,” J. Neo Soc., vol. 8, no. 3, pp. 189–203, 2023.
[4] W. Kustiawan, K. Rizky Ramadhani, S. Valentina Damanik, and A. Muharramsyah, “Pengaruh Iklan Politik Dalam Mengambil Aspirasi Rakyat,” SIBATIK: J. Ilm. Bid. Sos. Ekon. Budaya, Teknol. dan Pendidik., vol. 1, no. 8, pp. 1371–1380, 2022.
[5] G. P. A. Dipura, F. Amanda, M. R. Firmansyah, M. R. Rizky, and M. N. K. Jamal, “Teknologi Komputer Vision dalam Kamera Pengawas,” Karimah Tauhid, vol. 3, no. 3 SE-Articles, pp. 3754–3760, 2024.
[6] W. Swastika, A. Kurniawan, and H. Setiawan, “Deteksi dan Klasifikasi Merek Mobil untuk Penentuan Iklan Billboard Menggunakan Convolution Neural Network,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 4, pp. 701–708, 2020.
[7] Randy Moh Yusup, Aldof Faris Anugrah, D. D. Muslimah, S. M. W. N. Permana, and Shindi Yuliani, “Pendeteksi Objek Menggunakan OpenCV dan Metode YOLOv4-Tiny Untuk Membantu Tuna Netra,” J. Comput. Sci. Inf. Technol., vol. 1, no. 2, pp. 59–68, 2024.
[8] E. S. Manapa, E. A. M. Sampetoding, T. W. Sagala, and H. R. Taluay, “Ulasan Penelitian Sistem Operasi Waktu Nyata di Indonesia,” J. Dyn. Saint, vol. 5, no. 2, pp. 973–979, 2021.
[9] I. Maulana, N. Rahaningsih, and T. Suprapti, “Analisis Penggunaan Model YOLOv8 (You Only Look Once) Terhadap Deteksi Citra Senjata Berbahaya,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3621–3627, 2023.
[10] J. S. W. Hutauruk, T. Matulatan, and N. Hayaty, “Deteksi Kendaraan secara Real Time menggunakan Metode YOLO Berbasis Android,” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 9, no. 1, pp. 8–14, 2020.
[11] M. Ghulam, A. Binuri, M. Amirul, T. Haryanti, and A. Peningkatan, “Penerapan Algoritma Yolo V7 Sebagai Dekteksi Kecelakaan Pada Lalu Lintas,” Prosiding-Seminar Nas. Teknol. Inf. Ilmu Komput., vol. 2, no. 1, pp. 35–41, 2023.
[12] N. J. Hayati, D. Singasatia, and M. R. Muttaqin, “Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan,” Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 2, pp. 91–99, 2023.
[13] Primasdika Yunia Putra, A. S. Arifianto, Zilvanhisna Emka Fitri, and Trismayanti Dwi Puspitasari, “Deteksi Kendaraan Truk pada Video Menggunakan Metode Tiny-YOLO v4,” J. Inform. Polinema, vol. 9, no. 2, pp. 215–222, 2023.
[14] H. M. Zayani et al., “Unveiling the Potential of YOLOv9 through Comparison with YOLOv8,” IJISAE, vol. 12, no. 3, pp. 2845–2854, 2024.
[15] C.-Y. Wang, I.-H. Yeh, and H.-Y. M. Liao, “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,” 2024.
[16] M. R. Sholahuddin, F. Atqiya, S. R. Wulan, M. Harika, S. Fitriani, and Y. Sofyan, “Implementasi Sistem Identifikasi Senjata Real Time Menggunakan YOLOv7 dan Notifikasi Chat Telegram,” J. Inf. Syst. Res., vol. 4, no. 2, pp. 598–606, 2023.
[17] W. S. Sari and C. A. Sari, “Klasifikasi Bunga Mawar Menggunakan Knn Dan Ekstraksi Fitur Glcm Dan Hsv,” Skanika, vol. 5, no. 2, pp. 145–156, 2022.
[18] Muhammad Nur Ihsan Muhlashin and A. Stefanie, “Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Menggunakan YOLO V8,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1363–1368, 2023.
[19] W. Wijiyanto, A. I. Pradana, and S. Sopingi, “Perbandingan Data Untuk Memprediksi Ketepatan Studi Berdasarkan Atribut Keluarga Menggunakan Machine Learning,” JIKA (Jurnal Inform., vol. 8, no. 2, pp. 221–228, Apr. 2024, Accessed: May 26, 2024.
[20] G. Zeng, “On the confusion matrix in credit scoring and its analytical properties,” Commun. Stat. - Theory Methods, vol. 49, no. 9, pp. 2080–2093, May 2020.
[21] H. Tantyoko, D. K. Sari, and A. R. Wijaya, “Prediksi Potensial Gempa Bumi Indonesia Menggunakan Metode Random Forest Dan Feature Selection,” IDEALIS Indones. J. Inf. Syst., vol. 6, no. 2, pp. 83–89, 2023.
[22] S. Meldiyana, I. S. Fathina, R. Yuner, S. Rachmat, and M. Harika, “Line Crossing Detector System Pada Real-Time Situational Awareness Dengan Menggunakan Spatial Sample Difference Consensus,” JTT (Jurnal Teknologi Terapan, vol. 8, no. 1, p. 43, 2022.