| dc.description.abstract | Forest fires are a serious environmental issue in Indonesia, causing extensive impacts on ecosystems and human activities. Early detection of hotspots is an essential step in preventing forest fires. This study developed a hotspot detection system based on the Internet of Things (IoT) and optical technology, utilizing an AMG8833 infrared camera sensor integrated with an ESP32 microcontroller and the ThingSpeak platform. The system is designed to monitor environmental temperature, display data in real-time, and send fire warning notifications via Telegram, including GPS coordinate information. The results show that the system can detect temperatures above 35°C, trigger automatic alarms, and send notifications with high accuracy. Calibration results indicate a sensor accuracy of 99.14% compared to a standard thermogun, confirming that the AMG8833 is suitable for hotspot detection. Temperature data can be visualized on ThingSpeak with fast synchronization and low latency. Field testing demonstrated that the system successfully detected temperature increases caused by burning dry leaves at an effective distance of up to ±7 meters, with a response time of 2-56 seconds. Overall, the developed hotspot detection system is responsive, accurate, and reliable as an early forest fire detection device. The integration of IoT and optical technology enables automatic and real-time delivery of temperature, location, and warning information, making it effective for remote monitoring and forest fire prevention. | en_US |
