<![CDATA[This study aims to predict the Fine Fuel Moisture Code (FFMC) as an indicator of the ignitability level of natural fuels using the Random Forest algorithm. FFMC is an essential component of fire danger rating systems, used to describe the dryness level of fine fuels; therefore, accurate prediction is crucial for forest and land fire mitigation efforts. The research utilizes meteorological variables—including maximum temperature, relative humidity, wind speed, and rainfall—as input parameters. The model implementation is carried out in Google Colab using the Python programming language, accompanied by hyperparameter tuning through GridSearchCV to obtain the most optimal model configuration.The results indicate that relative humidity and rainfall are the most influential meteorological features in predicting FFMC. The developed Random Forest model demonstrates excellent performance, as reflected by a Mean Squared Error of 8,875 and an R-squared value of 0,974, which signifies a very high predictive capability. These findings confirm that the machine-learning approach based on Random Forest is able to produce precise FFMC estimations, thereby giving the model strong potential for integration into early warning systems for forest and land fires. Such integration would support decision-making processes, enhance the effectiveness of mitigation strategies, and strengthen preparedness in managing fire-related risks. Keywords: Fine Fuel Moisture Code, Fire Danger Prediction, Meteorological Features, Machine Learning, Random Forest.]]>
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