An Intelligent Case Based Reasoning Approach for Diagnosing Fish Quality Decline Using IoT - Charis Fathul Hadi; Dewi Mutamimah; Firman Hidayat (PT. Global Research Collaboration, 2026)
Indonesia ranks as one of the world’s largest fish producers, with an annual production of 6.54 million tons. Among the most economically valuable marine commodities is scad fish (Decapterus spp.). However, fish quality deterioration remains a major challenge due to environmental conditions, post-harvest handling, and inadequate storage methods. This study integrates Internet of Things (IoT) technology and Case-Based Reasoning (CBR) to diagnose fish quality deterioration in real time. Data were collected over a 24-hour period using IoT sensors that monitored temperature, humidity, pH, and ammonia levels. The CBR algorithm compared sensor data with historical cases through similarity measurements to provide diagnostic outcomes. Results indicate that between 07:00 and 09:00, fish maintained freshness (pre-rigor stage) with a similarity of 96.66%. However, quality decreased significantly from 12:00 to 06:00, reaching a similarity of 71.06% at 06:00. Findings highlight that temperature and ammonia levels are key factors driving spoilage, while humidity accelerates microbial growth and pH variation reflects rigor and post-rigor phases. The proposed system achieved a diagnostic accuracy of 96.47% compared to expert evaluations. These results demonstrate the system’s reliability in monitoring fish quality and its potential application in seafood supply chains. By minimizing spoilage, the system provides both economic and societal benefits through improved food safety and distribution efficiency. This study contributes a practical and scalable approach for intelligent food monitoring systems.