Integrated Fuzzy Logic Model for Smart Water Quality Monitoring and Floating Net Cage Optimization in Barramundi Aquaculture - Pramana, Rozeff; Alajuri, M Hasbi Sidqi (Informatika, Universitas Jenderal Soedirman, 2025)
Water quality and aquatic conditions are critical factors in the success of fish farming with Floating Net Cages (FNCs). However, manual monitoring is often delayed due to limited human resources, irregular measurement schedules, and dependence on manual sampling, which can result in late detection of deteriorating water quality and ultimately increase the risk of fish stress, disease outbreaks, and mortality. This study aims to develop an Internet-based water quality monitoring system, integrated with smartphones and PCs, to support rapid decision-making for FNC relocation when water conditions deteriorate. The system is equipped with sensors for temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, anemometer, and wind direction, and was field-tested for 36 days in sea-based Barramundi aquaculture. Decision-making was implemented using a Fuzzy Inference System (FIS) with input variables: temperature, DO, pH, and anemometer data, while the output variable was the FNC status: “Relocate” or “Remain.” Results indicated that water quality changes occurred across both short-term and long-term intervals, and during a 56-hour fuzzy simulation, 10 data points suggested “Relocate” while 46 data points indicated “Remain.” The novelty of this research lies in the integration of real-time IoT monitoring with fuzzy logic specifically for FNC relocation decision-making, bridging environmental sensing and intelligent decision support. These findings demonstrate that the proposed system is more effective and efficient than conventional methods, contributing to the advancement of intelligent aquaculture technologies.