Implementasi YOLOv8 untuk Deteksi Pelat Nomor dan Validasi Pajak Kendaraan

Abstract

The increase in the number of vehicles in Indonesia requires a more efficient administrative system for vehicle validity validation. Manual verification processes such as checking vehicle registration certificates and license plates by officers in the field are considered ineffective, prone to error, and time-consuming, especially when dealing with high volumes of vehicles. This study aims to develop a computer vision-based automated system capable of detecting vehicle license plates and independently validating tax status. The method used is the CRISP-DM method, which includes understanding requirements, data processing, modeling, evaluation, and implementation. The model used is YOLOv8 to detect the license plate area, and EasyOCR is used for alphanumeric character recognition. The research dataset consists of 587 secondary images and 15 primary images. The secondary data was divided into 70% training data, 20% validation data, and 10% test data. The YOLOv8 model was trained using the best combination of hyperparameters, namely 200 epochs, batch size 16, and learning rate 0.01, which produced a box loss value of 0.38. The tax status validation process is divided into four categories: active, expired, invalid, and no tax information available. Thus, this research can contribute to the development of an effective vehicle tax validation automation system that has the potential to be implemented in public administration services.