PERANCANGAN SISTEM MONITORING BERBASIS IoT DALAM RANGKA MITIGASI KERUSAKAN MUATAN PADA KAPAL MV. PAN FLOWER

Abstract

The safety of bulk cargo constitutes a critical aspect of commercial vessel operations, strictly regulated under the IMSBC Code and the Grain Code. Hygroscopic commodities such as Sun Flower Meal and Soya Bean Meal are highly susceptible to cargo sweat phenomena caused by uncontrolled temperature and humidity gradients within the cargo hold. The primary issue encountered on MV. Pan Flower was that cargo hold condition monitoring was still conducted manually using rudimentary measuring instruments and periodic logbook entries, thereby creating data blind spots, particularly during adverse weather conditions that prevented the crew from physically accessing the main deck area. This study aimed to design and develop a prototype of a Smart Maritime Cargo Monitoring System utilizing the Raspberry Pi Pico 2W microcontroller and SHT30 sensors, capable of monitoring cargo hold temperature and humidity autonomously and in real time. The research employed the Research and Development (R&D) methodology based on the Borg and Gall model. The test results demonstrated that the system achieved a mean error rate of 1.28% for temperature and 0.3% for relative humidity, well below the industrial instrumentation tolerance threshold of 5%. The Store and Forward algorithm successfully ensured zero data loss under signal blackout conditions, with a

failover transition time of 10,000 ms and resynchronization within 7,200 ms. The multi- tiered early warning system (SAFE, CAUTION, DANGER) operated responsively

through an acoustic buzzer and Blynk push notifications. Product feasibility validation yielded scores of 98% from expert lecturers and 97% from Nautical cadets, placing the prototype in the "Highly Suitable" category, thereby confirming its viability for implementation as a cargo damage mitigation instrument on bulk carrier vessels.