Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Esadhipa Raif Syihabuddin
Universits Dian Nuswantoro
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
EVALUASI SISTEM ALPR BERBASIS YOLOV10 PADDLEOCR UNTUK PENGENALAN PLAT NOMOR KENDARAAN INDONESIA Esadhipa Raif Syihabuddin; MUHAMMAD NAUFAL
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8073

Abstract

Automatic License Plate Recognition (ALPR) is an important component in intelligent transportation systems, utilized for traffic surveillance, automated parking, and law enforcement. This research develops an ALPR system based on YOLOv10l integrated with fine-tuned PaddleOCR to detect and recognize characters on Indonesian vehicle license plates. The dataset used consists of 532 Indonesian license plate images from Roboflow Universe, divided into 426 training images and 106 validation images. The YOLOv10l model was trained for 50 epochs using COCO pretrained weights, while PaddleOCR PP-OCRv4 was fine-tuned for 100 epochs on license plate crops from the dataset. Evaluation was conducted by comparing three OCR engines: fine-tuned PaddleOCR, EasyOCR, and Tesseract. Results show that the YOLOv10l model achieved an mAP@0.5 of 0.981, Precision of 0.920, and Recall of 0.943, with a Detection Rate of 97.17%. Fine-tuned PaddleOCR outperformed the other engines with a Readable OCR rate of 47.57% and Correct Recognition rate of 36.89%, followed by EasyOCR at 13.59% and Tesseract at 0.00%. This research confirms that fine-tuning PaddleOCR on a domain-specific dataset contributes positively to the accuracy of Indonesian license plate character recognition.
Co-Authors Muhammad Naufal