<![CDATA[This study designed an indoor environmental condition monitoring system based on IoT using the ESP32 microcontroller and DHT11 sensor, specifically tested to handle extreme temperature fluctuations in Indonesia's tropical climate. The system aims to proactively prevent mold growth on walls by providing early detection of condensation risks and dew points. Temperature and humidity data are periodically transmitted via Wi-Fi and stored in the Firebase Realtime Database for long-term trend analysis. Test results showed that the temperature sensor had an average error of 2.1% with a data transmission success rate of 98%. The implementation of this system offers direct benefits to residents by maintaining air quality and respiratory health through a significant reduction in mold frequency. Furthermore, the web dashboard visualization assists users in taking measurable preventive actions, such as more efficient ventilation management, thereby preserving the structural integrity of the building over the long term.]]>
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