IoT-Enabled Real-Time Monitoring and Tsukamoto Fuzzy Classification of Mandar River Water Quality via Web Integration for Sustainable Resource Management - Insani, Chairi Nur; Arifin, Nurhikma (Jurnal Teknik Informatika (Jutif), 2025)
This study presents the design and implementation of a real-time water quality monitoring system that utilizes pH, Total Dissolved Solids (TDS), and turbidity sensors, integrated with an ESP32 microcontroller. Sensor data are processed using the Tsukamoto fuzzy logic method to classify river water suitability into two categories: Suitable and Not Suitable. This approach effectively addresses imprecise and uncertain data, thereby producing more reliable classifications compared to conventional threshold-based methods. System validation was conducted through field testing over seven consecutive days at four different times of the day (morning, midday, afternoon, and evening), with results demonstrating stable performance. Recorded pH values ranged from 7.02 to 9.96, TDS values from 140 to 176 ppm, and turbidity levels between 4.00 and 5.15 NTU, indicating that the Mandar River remains within safe limits for daily use. The novelty of this study lies in the direct implementation of the Tsukamoto fuzzy logic method on a resource-constrained IoT device (ESP32), enabling edge-level classification with low latency and without full reliance on cloud computing. The system is designed to maintain decision reliability even under fluctuating sensor data, thus offering a practical and integrated solution for real-time monitoring. The main contribution of this work to computer science is the demonstration of lightweight embedded intelligent algorithms capable of running on constrained devices, the reinforcement of Explainable AI through transparent linguistic rules, and the integration of IoT with edge computing to support sustainable resource management in real-time.