An Iot-Based Forest Fire Prediction System Using Fuzzy Logic Method - Jefri Lianda; Hikmatul Amri; Adam Adam; Johny Custer; Dea Fitriana (Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri, 2025)
Forest and land fires represent a recurring environmental challenge in Indonesia, especially during the prolonged dry season. These incidents result in significant consequences, including the destruction of ecosystems, threats to human health, and considerable economic disruption. To address this problem, the present research focuses on the development of an Internet of Things (IoT)-based system designed to predict and monitor the risk of forest fires by implementing the Fuzzy Logic method. The prototype integrates several sensors, namely a DHT22 sensor for measuring temperature and humidity, an MQ-2 sensor for detecting gas and smoke concentrations, and a flame sensor for identifying the presence of fire. All sensors are connected to a NodeMCU ESP8266 microcontroller that serves as the core of data processing and wireless communication. The collected sensor data is evaluated using a Fuzzy Logic algorithm, which classifies the fire risk into three distinct levels: “Safe,” “Caution,” and “Hazardous.” Experimental testing demonstrates that the system responds effectively to fluctuations in temperature, humidity, smoke levels, and visible flame in real time, with alerts displayed through a web-based dashboard. The DHT22 sensor exhibits an average error rate between 4.8% and 5% for temperature readings and between 4.1% and 4.5% for humidity measurements. In addition, the flame sensor successfully detects fire sources at distances reaching 300 cm. The outcomes confirm that the system achieves a high degree of reliability and accuracy, thereby providing valuable support for early warning, strengthening preventive strategies, and assisting authorities in mitigating the severe impacts of forest and land fires.