Main Article Content
Abstract
Forest fires represent a significant threat that necessitates the implementation of rapid and accurate early detection systems to mitigate environmental and economic damage. This study develops a forest fire detection system based on the ESP32 microcontroller, employing the Mamdani fuzzy logic method to analyze environmental parameters, namely temperature, air humidity, and gas concentration, as indicators of fire risk. The system is powered by renewable energy sources, including solar panels and lithium batteries, to enable autonomous operation. Sensor calibration was conducted to enhance the precision and reliability of measurements. System testing was performed under various real-world environmental conditions to evaluate its performance. The results demonstrate that the system accurately classifies environmental status into three categories: Safe, Alert, and Danger, with a 100% accuracy rate. Additionally, the system integrates real-time early warning notifications through the Telegram platform, facilitating prompt responses to potential fire incidents. The adaptive nature of the fuzzy logic algorithm allows dynamic adjustment to fluctuations in environmental parameters. Overall, this research confirms the effectiveness of combining Internet of Things (IoT) technologies with fuzzy logic for proactive and reliable forest fire detection.