<![CDATA[Water quality is a crucial factor affecting the health and survival of freshwater ornamental fish. Manual monitoring is often ineffective because changes in water conditions can occur rapidly and numerical sensor readings are difficult to interpret directly. This study aims to implement the Mamdani Fuzzy Logic method in an Internet of Things (IoT)-based aquarium water quality monitoring system using Total Dissolved Solids (TDS) and temperature sensors. Sensor data are processed through fuzzification, rule-based inference, and defuzzification to classify water quality into three categories: poor, normal, and good. The monitoring results are then displayed in real time through a web-based dashboard. Experimental results indicate that the TDS sensor achieved an average error of 3%, while the temperature sensor achieved an average error of 2.6%. Furthermore, the implementation of the Mamdani Fuzzy Logic method produced an average error of only 0.6% compared with MATLAB simulation results. These findings demonstrate that the proposed system is capable of monitoring aquarium water quality accurately and providing clear decision support for users in maintaining optimal water conditions. The main contribution of this study is the integration of the Mamdani Fuzzy method into an IoT-based monitoring system, implemented directly on the ESP32 microcontroller for real-time inference, unlike previous studies that typically process fuzzy data on servers or cloud platforms. This approach achieves accuracy comparable to MATLAB simulation with an average error of only 0.6%.]]>
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