Smart Air Purifier Control System Based on Mamdani Fuzzy Inference System

Indoor air quality has become a critical concern, particularly after the COVID-19 pandemic, due to its direct impact on respiratory health. Conventional air purifiers generally operate using static or manual control mechanisms, resulting in inefficient energy consumption and suboptimal purification performance. This study proposes a smart air purifier control system based on fuzzy logic using the Mamdani inference method. The system utilizes smoke concentration levels and room size as input variables to automatically determine the optimal fan speed. The fuzzy logic process consists of fuzzification, rule-based inference, and defuzzification using the centroid method. The proposed approach allows the system to handle uncertainty and gradual environmental changes effectively. The results indicate that the fuzzy logic-based controller can adaptively regulate fan speed, improving air purification efficiency while reducing unnecessary energy usage compared to conventional control methods.