Application of Mamdani Fuzzy Logic in Monitoring Dissolved Oxygen, pH, and Temperature in Koi Cyprinus rubrofuscus Raising Ponds

Water quality is very important for the success of ornamental fish rearing, especially in raising koi fish Cyprinus rubrofuscus. This research develops a water quality monitoring system using the Mamdani fuzzy logic method based on three main parameters, namely temperature, pH, and dissolved oxygen (DO). The system was designed to handle the uncertainty and indecisiveness often encountered in water quality assessment, particularly in aquaculture environments. Temperature parameters are classified into cold, normal, and hot categories; pH into acidic, neutral, and alkaline categories; and DO into low, medium, and high categories. Simulation and implementation of the fuzzy inference system were conducted using MATLAB software. Through the process of fuzzification, rule-based inference, and defuzzification, the system successfully predicts the water quality status from bad, normal and good categories. The system is proven to be effective in evaluating water quality status and has the potential to support smarter and more adaptive management of koi fish farming environments.