<![CDATA[Traffic congestion caused by the rapid increase in motor vehicles without proportional infrastructure development demands a more efficient and accurate vehicle identification system. One crucial component is the Vehicle Registration Number (TNKB), which serves as the official identifier. License Plate Recognition (LPR) technology provides a Smart Mobility solution that enables automatic reading of TNKB from digital images. This study aims to develop an automatic license plate recognition system in the "K" region of Kudus City using YOLO object detection and EasyOCR-based Optical Character Recognition (OCR). YOLO is used to detect license plate regions in both images and video in real time, while OCR is applied to recognize the characters. Previous studies have shown that YOLOv4 and YOLOv8 models achieve detection accuracies above 90% and can operate on low-resource devices under poor lighting conditions. This system is expected to improve the efficiency of vehicle data recording, reduce manual errors, and support the integration of smarter transportation systems. In conclusion, the implementation of LPR using YOLO and OCR shows strong potential for application in local traffic environments such as Kudus City.]]>
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