<![CDATA[SMK Negeri 3 Lhokseumawe is located in a high activity area surrounded by hotels, workshops and residential areas. Based on internal school data, student complaints related to respiratory problems increased significantly from 8% in 2020 to 30% in 2024. This condition shows the urgency of the need for an air quality monitoring system that is adaptive, efficient, and can operate in real-time in the school environment. This research aims to develop an Internet of Things (IoT)-based air pollution monitoring and early detection system with the integration of Takagi Sugeno Kang (TSK) fuzzy method. The system uses an ESP32 microcontroller connected to MQ-135 (CO₂), MQ-7 (CO), GP2Y1010AU0F (PM10), and DHT22 (temperature and humidity) sensors. In contrast to conventional approaches that only read raw data or rely on fixed thresholds, the TSK fuzzy method is able to adaptively process multivariate data and produce more precise air quality classifications. Data is sent in real-time to the server and displayed via website and LCD. Tests were conducted for 7 hours with 100 samples under relatively controlled environmental conditions. The implementation results show that the system runs stably and accurately, one of which is the measurement of CO 8.26 ppm, CO₂ 587 ppm, PM10 36.34 µg/m³, temperature 29.60°C, and humidity 76.50%, which is classified as “Fair” based on a fuzzy value of 2.303735. This research fills the literature gap by optimizing the TSK fuzzy method on a resource-limited device (ESP32), and offers novelty in the presentation of air quality information quickly and contextually to support health risk mitigation in educational environments.]]>
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