Implementasi Fuzzy Sugeno Berbasis IoT untuk Peringatan Kualitas Air Akuarium Ikan Mas Koki - Muhammad Taufikur Rahman; Febi Yanto; Elin Haerani (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
The manual monitoring of aquarium water quality is often ineffective due to time constraints and the potential delays in detecting critical parameter changes that can threaten fish health. This research develops a real-time water quality monitoring system for goldfish aquariums based on the Internet of Things (IoT) using the Sugeno fuzzy logic method. The system utilizes an Arduino Uno R4 WiFi microcontroller to process data from turbidity, Total Dissolved Solids (TDS), and water temperature sensors. The Sugeno fuzzy method is chosen for its ability to produce precise numerical outputs based on fuzzy rules. To assess water quality, the sensor data undergoes fuzzification, rule evaluation, implication/aggregation function application, and defuzzification stages. The measurement results are then processed in real-time and sent via WiFi connection to the Blynk application, which serves as a monitoring medium and sender of warning notifications to users when water quality falls outside safe limits, while information is also displayed on the OLED screen of the system. Water quality assessment is classified based on fuzzy output values into several condition categories: 0-20 (Very Good), 21-40 (Good), 41-60 (Fair), 61-80 (Poor), 81-100 (Very Poor). Based on the test results, the system has been proven to effectively detect and classify water quality conditions with high accuracy, as well as provide effective warning notifications. This system is expected to assist aquarium owners in maintaining optimal environmental conditions for the health of goldfish in an automatic, sustainable, and efficient manner.