Published: 2024-12-01
Vehicle License Plate Object Detection for Vehicle Registration Using Fuzzy Logic
DOI: 10.35870/ijsecs.v4i3.3055
Downloads
Abstract
Object detection of vehicle license plates plays a role in the efficiency of vehicle data collection systems. There are many factors that make the accuracy and speed of detection on vehicle license plates less than optimal, causing errors in the detection process. The factors that affect the accuracy of object detection of vehicle license plates include clarity, lighting, shadows, color, font type, weather, and others. Based on the advantages of the Fuzzy Logic approach in handling various vague factors and uncertain data, it is hoped that this method can help the detection process to be more accurate and faster. This research aims to develop a method for detecting vehicle license plate objects using the Fuzzy Logic approach so that it can be applied in diverse environments to produce data with consistent accuracy. This research involves the development of software integrated with computers and cameras for vehicle license plate recognition, and also takes some data sources and code from libraries already available in the programming language used. The results of the tests conducted, detection using this Fuzzy Logic approach has an accuracy rate of up to 93.33% and the accuracy of reading the text stored in the database reaches 63.66%.
Keywords
Detection System ; Fuzzy Logic ; Plate Detection ; Object Detection ; Vehicle Data Collection
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 4 No. 3 (2024)
-
Section: Articles
-
Published: %750 %e, %2024
-
License: CC BY 4.0
-
Copyright: © 2024 Authors
-
DOI: 10.35870/ijsecs.v4i3.3055
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Fiky Alannuari
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Frencis Matheos Sarimole
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Dadang Iskandar Mulyana
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
-
Rohan, A., Rabah, M., & Kim, S. H. (2019). Convolutional neural network-based real-time object detection and tracking for parrot AR drone 2. IEEE access, 7, 69575-69584. https://doi.org/10.1109/ACCESS.2019.2919332
-
Hindarto, D., & Santoso, H. (2021). Plat Nomor Kendaraan dengan Convolution Neural Network. Jurnal Inovasi Informatika, 6(2), 1-12.
-
Muchyi, Y. A., & Siswono, H. (2022). Deteksi Ganjil Genap Pada Plat Nomor Kendaraan Menggunakan Metode K-Nearest Neighbor (KNN): Array. Jurnal Ilmiah Komputasi. https://doi.org/10.32409/jikstik.21.2.3060
-
Kusnantoro, K., Rohana, T., & Kusumaningrum, D. (2022). Implementasi Metode Tesseract OCR (Optical Character Recognition) untuk Deteksi Plat Nomor Kendaraan Pada Sistem Parkir. Scientific Student Journal for Information, Technology and Science, 3(1), 59-67.
-
Aprilino, A. (2022). Implementasi Algoritma Yolo Dan Tesseract Ocr Pada Sistem Deteksi Plat Nomor Otomatis. Jurnal Teknoinfo, 16(1), 54-59. https://doi.org/10.33365/jti.v16i1.1522
-
Jonathan, M., Hafidz, M. T., Apriyanti, N. A., Husaini, Z., & Rosyani, P. (2023). MENDETEKSI PLAT NOMOR KENDARAAN DENGAN METODE YOLO (You Only Look Once) DAN SINGLE SHOT DETECTOR (SSD). AI dan SPK: Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan, 1(1), 105-111.
-
Li, H., Qiu, J., Li, T., Xie, G., Wang, D., & Wang, W. (2021). Multiple-criteria evaluation of thin-walled energy-absorbing structures of train under fuzzy environment: modeling and algorithm. IEEE Access, 9, 150393-150402. https://doi.org/10.1109/ACCESS.2021.3125397
-
Aida, N. R., & Hardiyanto, S. (2024). Ganti warna pelat nomor kendaraan warna putih dimulai 2022, ini penjelasan Korlantas Polri. Kompas.com. Retrieved July 10, 2024, from https://www.kompas.com/tren/read/2022/01/24/083000865/ganti-warna-pelat-nomor-kendaraan-warna-putih-dimulai-2022-ini-penjelasan?page=all#google_vignette
-
Saputra, V. P., Latifa, U., & Ibrahim, I. (2023). Simulasi Detection Counter Pada Objek Kendaraan Motor Dan Mobil Menggunakan Metode Convolutional Neural Network Berbasis Python. Jurnal Ilmiah Wahana Pendidikan, 9(16), 760-766. https://doi.org/10.5281/zenodo.8265040
-
Permadi, H. S., Ridwan, M., & Rismaningsih, F. (2021). Implementasi logika fuzzy pada alat cuci tangan otomatis portabel dengan sistem monitoring berbasis Android. Jurnal Buana Informatika, 12(2), 106–115. https://doi.org/10.24002/jbi.v12i2.4768
-
Romadhon, M., Indra, J., & Novita, H. (2023). Implementasi Fuzzy Logic Tsukamoto pada Deteksi Kondisi Badan Berdasarkan Suhu Tubuh. Scientific Student Journal for Information, Technology and Science, 4(1), 57-65.
-
Setya Budi, A. H., Baiquni, M. A., Mulyanti, B., & Nasution, M. F. (2024). Sistem deteksi laju dan plat nomor kendaraan berbasis video rekaman menggunakan YOLOv5-DeepSORT dan HyperLPR. Telekontran: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan, 11(2), 140–149. https://doi.org/10.34010/telekontran.v11i2.10900
-
Rochmah, A. U., Junus, M., & Imammuddin, A. M. (2021). Sistem Kendali dan Monitoring Garasi Menggunakan Metode Fuzzy Logic Berdasarkan Jarak dan Kecepatan User. Journal of Telecommunication Network (Jurnal Jaringan Telekomunikasi), 11(4), 208-213.
-
Darmawan, R., Taqwa, A., & Endri, J. (2016). Rancang bangun sistem palang otomatis dengan pengenalan plat kendaraan. Retrieved from http://ojs.uho.ac.id/index.php/jfe/36
-
Nasron, N., Suroso, S., & Putri, A. R. (2019). perancangan logika FUZZY untuk sistem pengendali kelembaban tanah dan suhu tanaman. Jurnal Media Informatika Budidarma, 3(4), 307-312.
-
Romadhon, N. A. G., Usman, K., & Patmasari, R. (2020). Deteksi Plat Nomor Kendaraan dengan Hough Transform dan Harris Corner Menggunakan Akuisisi Melalui Raspberry Pi. TELKA-Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol, 6(2), 93-103. https://doi.org/10.15575/telka.v6n2.93-103.
-
Cahyo, N. D. (2019). Pengenalan Nomor Plat Kendaraan Dengan Metode Optical Character Recognition. Ubiquitous: Computers and its Applications Journal, 2(1), 75-84.
-
Fauzan, M. R., & Wibowo, A. P. W. (2021). Pendeteksian Plat Nomor Kendaraan Menggunakan Algoritma You Only Look Once V3 Dan Tesseract. Jurnal Ilmiah Teknologi Infomasi Terapan, 8(1), 57-62. https://doi.org/10.33197/jitter.vol8.iss1.2021.718.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.