Pengembangan Sistem Monitoring Kualitas Air dan Kendali Pakan Otomatis untuk Budidaya Ikan Berbasis Internet of Things

Abstract

Water quality monitoring is crucial in aquaculture to maintain fish health and increase productivity. This study developed an Internet of Things (IoT)-based water quality monitoring and automatic feeding system using an ESP32 microcontroller, DS18B20 temperature sensor, TDS sensor, turbidity sensor, and SG90 servo motor. The Blynk platform was used as an interface to monitor real-time data, set feeding intervals, and send notifications. The system was developed using the prototyping method, including analysis, design, implementation, testing, and refinement. Sensor calibration was conducted by comparing the temperature sensor with a digital thermometer, the TDS sensor with a digital TDS meter, and the turbidity sensor with digital turbidity meter. A total of 30 measurement samples were collected to calculate deviations. The operational ranges detected were 20–30 °C, 220–260 ppm, and 2–4 NTU. Results showed average deviations of 2.04 °C (92.44%), 2.84 ppm (96.21%), and 0.17 NTU (98.11%). The system provides reliable monitoring and programmable feeding intervals, enhancing efficiency in aquaculture management.