Fuzzy Logic-Based Decision Support System for Adaptive Plant Watering Volume in IoT Environments - Niefa Efrilia Violenic; Gesit Tri Nugroho; Aldy Arifyan Putra; Ahmad Fauzan (Journal of Applied Science, Technology & Humanities | JASTH, 2026)
Conventional irrigation systems often result in inefficient water use due to their inability to adapt to dynamic and uncertain environmental conditions. This study aims to design and simulate an adaptive smart irrigation system using Mamdani Fuzzy Logic Controller (FLC) in an Internet of Things (IoT) architecture. This methodology integrates four environmental parameters, namely Soil Moisture, Air Temperature, Air Humidity, and Light Intensity to calculate the appropriate watering duration, effectively reducing the risk of false positives associated with traditional two-input systems. The mathematical model was verified and simulated using MATLAB Fuzzy Logic Toolbox, with the Centroid defuzzification method. The results show that in extreme testing scenarios, the system successfully calculated the appropriate watering duration of 18 seconds. This analytical calculation perfectly aligns with the MATLAB simulation, demonstrating 100% accuracy with no error deviation. In conclusion, the proposed four-input Mamdani Fuzzy Logic controller effectively reduces data ambiguity and optimizes agricultural water consumption, establishing a solid mathematical foundation for future IoT hardware implementation in precision agriculture.