Pengembangan Sistem Pengenalan Plat Nomor Indonesia Menggunakan YOLOv8 dan EasyOCR
:
https://doi.org/10.32409/jikstik.23.4.3659
Keywords:
Gate system, Machine Learning, YOLOv8, EasyOCR, CRISP-DMAbstract
Sistem gerbang di Indonesia saat ini masih mengandalkan metode tradisional seperti gerbang manual atau teknologi RFID, yang memiliki keterbatasan dalam hal efisiensi dan keamanan. Penelitian ini bertujuan untuk mengembangkan solusi alternatif dengan menggabungkan teknologi text recognition berbasis machine learning dan kerangka kerja CRISP-DM. Metode yang digunakan melibatkan pendekatan multi-metode, yaitu metode terapan dan eksperimental. Metode terapan menggunakan kerangka kerja CRISP-DM untuk mengelola proyek, sementara metode eksperimental melibatkan pengujian model pada data yang dikumpulkan secara manual di lingkungan luar. Dataset yang digunakan adalah berjumlah 448 gambar yang dibagi kedalam tiga bagian berbeda yaitu train, validation, dan testing. Data plat nomor dikumpulkan secara manual dari lingkungan luar untuk mencerminkan kondisi kehidupan nyata, Algoritma yang diimplementasikan untuk mendeteksi plat nomor pada gambar kendaraan adalah algoritma YOLO V8. Sedangkan algoritma yang digunakan untuk text recognition adalah algoritma EasyOCR. Flask akan digunakan untuk mendistribusikan model secara berbasis web. Kerangka kerja CRISP-DM akan digunakan untuk memastikan proyek dapat selesai dilaksanakan. Pada bagian eksperimen, 100 gambar diuji untuk dapat mendapatkan perkiraan akurasi dari hasil sistem deteksi. Hasil pengujian menunjukkan bahwa model deteksi memiliki akurasi sekitar 99%, sementara text recognition mencapai akurasi sekitar 81%. Dengan memanfaatkan kerangka kerja CRISP-DM, kami berhasil mengembangkan sistem pendeteksi plat nomor berbasis web yang dapat memudahkan akses pengguna. Penelitian ini merupakan upaya untuk mengembangkan solusi alternatif untuk Sistem Gerbang Indonesia dengan mengembangkan machine-learning text recognition yang dikombinasikan dengan kerangka kerja CRISP-DM.
Downloads
References
L. Masello, G. Castignani, B. Sheehan, F. Murphy, and K. McDonnell, “On The Road Safety Benefits Of Advanced Driver Assistance Systems In Different Driving Contexts,” Transp Res Interdiscip Perspect, vol. 15, p. 100670., 2022.
F. Ahda, M. Abdurohman, and A. G. Putrada, “Evaluation of Active RFID as Vehicle Identification at Parking Gates Using Queuing Theory,” in Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019, 2019, pp. 1–6.
V. Kumar Chinnaiyan, S. Balaji, R. Beny, and E. Kavin Jayasuriya, “Automatic number plate recognition system,” in 12th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2021, 2021, pp. 28–48.
A. Naureen, K. C. Krishna, N. S. Teja, L. Mahesh, and K. Vamshi, “College Bus Number Plate Registration Detection Using YOLO-V8,” in 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1–7.
A. Chawla, A. Gupta, K. S. Shushrutha, and Mohana, “Intelligent Information Retrieval: Techniques for Character Recognition and Structured Data Extraction,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 9, no. 7, pp. e452–e459, 2022.
M. Sohan, T. Sai Ram, and Ch. V. Rami Reddy, “A Review on YOLOv8 and Its Advancements,” in nternational Conference on Data Intelligence and Cognitive Informatics, 2024, pp. 529–545.
K. C. Aquitan, C. A. Muaña, D. Angela Velasquez, and I. D. Kim Machica, “YOLO v8-based Real-Time Helmet Detection for Enhanced Traffic Monitoring and Surveillance”,
V. K. Singh, A. Singh, and K. Joshi, “Fair CRISP-DM: Embedding Fairness in Machine Learning (ML) Development Life Cycle,” in Proceedings of the Annual Hawaii International Conference on System Sciences, 2022, pp. 1531–1540.
Y. A. Singgalen, “Analisis Performa Algoritma NBC, DT, SVM Dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 3, pp. 1634–1646, 2022.
I. Nurhaida, I. Nududdin, and D. Ramayanti, “Indonesian License Plate Recognition With Improved Horizontal-Vertical Edge Projection,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 811–821, 2020,
Sheetal S. Patil et al., “Vehicle Number Plate Detection using YoloV8 and EasyOCR,” in 14th International Conference on Computing Communication and Networking Technologies , 2023, pp. 1–4.
S. A. Kahie, A. A. Nor, A. H. Hasan, A. M. Abdi, L. M. Hassan, and M. A. Mohamud, “A Smart Access Control for Restricted Buildings Using Vehicle Number Plates Recognition System,” in 2021 1st International Conference on Emerging Smart Technologies and Applications, eSmarTA 2021, Institute of Electrical and Electronics Engineers Inc., Aug. 2021.
H. Moussaoui et al., “Automated Vehicle Identification By Integrating Yolo V8 And Ocr Techniques For High precision License Plate Detection And Recognition,” Sci Rep, vol. 14, no. 1, p. 14389, Dec. 2024.
Y. Christian and K. O. Y. R. Qi, “Penerapan K-Means Pada Segmentasi Pasar Untuk Riset Pemasaran Pada Startup Early Stage Dengan Menggunakan CRISP-DM,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 4, p. 966, 2022.
A. Nadali, E. N. Kakhky, and H. E. Nosratabadi, “Evaluating The Success Level Of Data Mining Projects Based On CRISP-DM Methodology By A Fuzzy Expert System,” in ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology, 2011, pp. 161–165.
X. Hou, M. Fu, X. Wu, Z. Huang, and S. Sun, “Vehicle License Plate Recognition System Based On Deep Learning Deployed To PYNQ,” in ISCIT 2018 - 18th International Symposium on Communication and Information Technology, 2018, pp. 79–84.
Y. Y. Lee, Z. Abdul Halim, and M. N. Ab Wahab, “License Plate Detection Using Convolutional Neural Network-Back to the Basic With Design of Experiments,” IEEE Access, vol. 10, pp. 22577–22585, 2022.
A. Abujaber, A. Fadlalla, D. Gammoh, H. Abdelrahman, M. Mollazehi, and A. El-Menyar, “Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach,” Scand J Trauma Resusc Emerg Med, vol. 28, no. 1, pp. 1–10, 2020, doi: 5.
Chandler Timm C. Doloriel and R. D. Cajote, “Improving the Detection of Small Oriented Objects in Aerial Images,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 176–185. [Online].
R. Shrestha and J. M. Chatterjee, “Heart Disease Prediction System Using Machine Learning Model,” LBEF Research Journal of Science, Technology and Managemen, vol. 1, no. 2, pp. 115–132., 2019.
O. E. Taylor, P. S. Ezekiel, and F. B. D. Okuchaba, “A Model To Detect Heart Disease Using Machine Learning Algorithm,” International Journal of Computer Sciences and Engineering, vol. 7, no. 11, pp. 1–5, 2019.
H. Kwon and J. W. Baek, “Adv-Plate Attack: Adversarially Perturbed Plate for License Plate Recognition System,” J Sens, vol. 2021, 2021,
Downloads
Published
How to Cite
Issue
Section
Categories
Abstract View: 0 times
