Automation of Water Quality Recovery in Vannamei Shrimp Aquaculture using the KNN Algorithm and Fuzzy Logic

Vannamei shrimp play a crucial role in Indonesia’s fisheries and export industries. Despite their high potential, shrimp aquaculture still faces significant challenges, particularly disease susceptibility caused by fluctuations in water quality and pond environmental conditions. This study aims to develop a system capable of automatically monitoring and restoring water quality using the K-Nearest Neighbors (KNN) algorithm and fuzzy logic. The research adopts a research and development (R&D) approach, which includes problem analysis, data collection, system design, development, testing, evaluation, and implementation. The system employs the KNN algorithm with K=5K = 5K=5 to diagnose water quality conditions, while fuzzy logic is used to automatically control aerators, pumps, and drainage systems. The sensors utilized include salinity, pH, temperature, dissolved oxygen, and turbidity, all integrated through an ESP32 microcontroller within an Internet of Things (IoT) network. The results demonstrate that the system achieves a diagnostic accuracy of 95% and is capable of automatically controlling recovery devices. With real-time and automated operation, the system effectively maintains pond water quality, thereby improving productivity and the overall success of vannamei shrimp aquaculture.