<![CDATA[As a maritime nation, Indonesia holds substantial marine-resource potential, yet its distribution faces serious challenges because fishery commodities are highly perishable and prone to rapid quality decline. Failure to maintain consistent temperatures throughout the cold chain frequently causes significant economic losses for business operators. This study aims to design and build an Intelligent Cold Chain Control System Based on Data Fusion for Refrigerated Truck Fleets using an ESP32-S3 microcontroller. The system integrates Internet of Things (IoT) technology with a data fusion technique that combines readings from an RTD PT100 temperature sensor paired with a MAX31865 module, an MPU6050 vibration sensor, and a NEO-8M GPS module for real-time location tracking, using an SD card as a buffer medium and the MQTT protocol for transmitting data to a server. The research method applied is the Prototype method, comprising requirements analysis, initial system design, prototype development, user evaluation, prototype improvement, prototype testing, and implementation. Data were collected through field observation, interviews, and a literature study at CV. Mutiara Samudera Indonesia, Tegal. Test results show that the PT100 sensor was able to read temperatures down to −15.5°C with a deviation of only 0.2°C against a reference instrument, the NEO-8M GPS module successfully displayed location coordinates within a tolerance of a few meters compared with Google Maps, and the MPU6050 sensor was able to detect vibration changes on three axes in a stable manner. This prototype is expected to support quality monitoring and distribution efficiency for marine commodities carried by refrigerated truck fleets.]]>
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