<![CDATA[The increasing volume of goods and growing demand for rapid distribution in modern logistics systems require automated sorting technologies that are fast, accurate, consistent, and capable of integrated operational data management. Conventional sorting processes that rely heavily on human operators are prone to destination identification errors, processing delays, and limited real-time monitoring capabilities. This study develops an automated parcel sorting system based on distribution destination identification integrated with real-time data logging to support the implementation of smart logistics. The proposed system integrates an Arduino UNO as the main controller, a GM66 barcode scanner for parcel identification, infrared sensors for object detection, a DC motor as the conveyor mechanism, a servo motor as the sorting actuator, and an ESP8266 module for cloud connectivity using Google Sheets. A classification algorithm was implemented to map destination codes into three distribution regions, namely East Java (JATIM), Central Java (JATENG), and the Special Region of Yogyakarta (DIY), and subsequently control the sorting actuator according to the identified destination. In addition to automated sorting, the system simultaneously records operational data on a cloud platform, enabling real-time monitoring and traceability of sorting activities. Experimental testing involving 30 parcel samples achieved a barcode reading accuracy of 93.33%, while the sorting process achieved a 100% success rate for successfully identified parcels. The average barcode identification time was 1 second, with an actuator response delay of 0.5 seconds, resulting in an overall system response time of 1.5 seconds. These results demonstrate that the integration of embedded systems, barcode identification, automated sorting, and Internet of Things (IoT) technology can provide a responsive sorting mechanism while enabling real-time operational data acquisition. The modular architecture provides potential for scalability to additional distribution destinations, although such scalability has not yet been experimentally validated.]]>
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