<![CDATA[Water quality is a factor that affects the survival of tilapia in grow-out ponds. Monitoring water quality manually can cause changes in water conditions to be detected late, which can potentially reduce aquaculture productivity. This study aims to design and build an Internet of Things (IoT)-based water quality monitoring system combined with the Mamdani Fuzzy Logic method as the basis for automatic decision-making. Here, I use an ESP32 microcontroller system connected to a DS18B20 temperature sensor, pH sensor, float switch, and a two-channel relay to control the drainage pump and water filling pump. Temperature and pH data are processed through fuzzification, inference, and defuzzification stages to classify water quality into good, moderate, and poor categories. Measurement information is sent to the Blynk application so that users can monitor pond conditions in real-time. The implementation results show that the system can continuously monitor water quality and automatically carry out water changes when the water quality is poor. This way, the developed system can help farmers maintain water quality more effectively, efficiently, and sustainably.]]>
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