<![CDATA[Manual water meter reading remains a challenge for Perumdam Tirta Kerta Raharja due to its labor-intensive process, susceptibility to human errors, and inefficiency. This study aims to develop an automated water meter reading system using YOLOv9 and a microcontroller to improve efficiency and data accuracy. The model was trained using a dataset of water meter images under various lighting conditions and viewing angles. Evaluation results indicate that the 20-epoch configuration is the best model, achieving 99,91% accuracy, 91,16% average precision, and 91,04% average recall. The developed system successfully detects digits in real-time with high accuracy when deployed on a Raspberry Pi-based platform. However, the model still faces challenges in detecting the Background class. With further optimization, this system can be widely implemented to enhance operational efficiency in Perumdam and related industries.]]>
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