<![CDATA[Kitchen air quality is an important factor affecting the health and safety of occupants due to exposure to LPG gas, combustion smoke, volatile organic compounds (VOC), as well as changes in temperature and humidity during cooking activities. Conventional air quality monitoring methods have limitations in providing real-time environmental information and early warnings when air quality begins to deteriorate. This study aims to develop an Internet of Things (IoT)-based kitchen air quality monitoring and evaluation system using MQ-6, MQ-135, and DHT22 sensors integrated with an ESP32 microcontroller. The proposed system classifies air conditions into three categories: normal, warning, and hazardous. Air quality information is displayed on an LCD and transmitted to users via Telegram notifications, while hazardous conditions trigger an audible alarm through a buzzer. To improve air circulation, an ON-OFF Hysteresis control method is implemented on two exhaust fans operating in stages, where the first exhaust fan is activated under warning conditions and both exhaust fans are activated under hazardous conditions. Furthermore, the collected monitoring data are analyzed using the Random forest algorithm to evaluate air quality patterns, classify environmental conditions, and identify the most influential parameters through feature importance analysis. The results indicate that the proposed system is capable of performing real-time air quality monitoring, providing environmental condition notifications, and automatically controlling ventilation according to the detected risk level. The integration of multi-sensor monitoring, ON-OFF Hysteresis control, and Random forest analysis enhances the system’s capability from conventional monitoring to a more informative and adaptive air quality evaluation platform for kitchen environments]]>
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