Sugeno Fuzzy Logic for IoT-based Chicken Farm Drinking Water Quality Monitoring - Rosmasari; Nur Rahmad , Didi; Prafanto, Anton; Khoirunnita, Aulia; Jamil, Muh (Indonesian Journal of Data and Science, 2025)
Introduction: The quality of drinking water plays a crucial role in the health and productivity of broiler chickens. In Indonesia, many poultry farms still rely on manual water testing using litmus paper, which may yield inaccurate results. This study aims to develop an Internet of Things (IoT)-based system integrated with the Sugeno fuzzy logic method to monitor and assess the quality of drinking water for broiler chickens in real time. Methods: An IoT prototype was developed using an ESP32 microcontroller, pH and turbidity sensors, and a cloud-based mobile application. Water quality data from 1,975 samples were collected over three days from a broiler farm in East Kalimantan using water sourced from a former mining lake. The system applies the Sugeno fuzzy inference system with 15 expert-defined rules to classify water quality into four categories: Very Good, Good, Bad, and Very Bad. Performance was evaluated using a Confusion Matrix. Results: The system achieved a classification accuracy of 96.76%, precision of 97.52%, recall of 98.79%, and F1-score of 98.15%. The results demonstrate the system's effectiveness in identifying water quality, with the majority of predictions falling into the correct class. The system also successfully transmitted real-time data to an Android application for monitoring purposes. Conclusions: The integration of IoT and Sugeno fuzzy logic provides a reliable, accurate, and scalable solution for real-time water quality monitoring in poultry farming. This system enhances decision-making for farmers, supports animal welfare, and can be further developed to include additional environmental parameters for broader livestock health monitoring