<![CDATA[Poor air quality in laundry rooms is generally caused by high temperature and humidity levels as well as potential exposure to carbon monoxide (CO) gas from equipment operations, which can reduce working comfort and increase health risks if not properly managed. This study proposes a prototype of an automatic ventilation system based on the Internet of Things (IoT) using a rule-based approach to support adaptive air quality control. The contribution of this research lies in the design of decision rules that integrate gas concentration, temperature, and humidity parameters into three condition levels (normal, alert, and danger), as well as in the comprehensive evaluation of system performance as a reference for the development of similar systems. The system was developed using an ESP32 microcontroller with MQ-135 and DHT22 sensors, equipped with an exhaust fan actuator and warning devices, and real-time monitoring through the Blynk IoT platform. Testing was conducted over three days with 360 measurement data points. The results show that the DHT22 sensor achieved measurement accuracy of 97.84% for temperature and 84.65% for humidity, while the MQ-135 sensor reached 92.44%. IoT communication performance was also stable, with 0% packet loss and an average latency of 194.25 ms. In the applied testing scenarios, the rule-based classification demonstrated full conformity with the predefined criteria; however, generalization of the findings still requires further validation under broader operational conditions and environmental variations. Overall, the findings indicate that the proposed system is responsive and reliable, and has the potential to serve as a practical and relatively low-cost solution for monitoring and controlling air quality in laundry rooms.]]>
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