<![CDATA[PT Interskala Mandiri Indonesia relies on manual input of Price Look Up (PLU) codes on the keypad for digital weighing, which results in human errors and lower operational efficiency. This study presents the development of an AIoT-based digital scale that integrates YOLOv8 for automatic fruit classification and leverages a Virtual Private Server (VPS) as a centralized data management infrastructure. The ADDIE model is used as the research and development framework. The hardware is built using an ESP32 NodeMCU-32S microcontroller, an ESP32-S3 CAM for image capture, and a load cell with an HX711 module for precise weight measurement. The YOLOv8n model was trained on five fruit classes (fuji apple, orange, lemon, century pear, and dragon fruit) and deployed on a VPS backend via Flask API. Receipt printing is performed through a Bluetooth T3 thermal printer using RawBT software, while monitoring is conducted through a React.js dashboard. Test results show that YOLOv8n achieved mAP@50 of 99.5%, precision of 99.97%, recall of 100%, and F1-score of 100%. The load cell provided 99.74% accuracy with a 0.26% error tolerance. All 25 Black Box Testing scenarios returned a Successful status. Average end-to-end latency was 7.55 seconds. The system proved capable of eliminating manual PLU input, centralizing transaction management, and providing a digital scale modernization solution for the retail industry.]]>
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