Forest Fire Prediction Using K-Mean Clustering and Random Forest Classifier

Prasetyo Mimboro, Bayu Yanuargi, Romdhi Surimba, Kusrini Kusrini, Khusnawi Khusnawi


Prediction of fire forest will be needing several parameters that located on the same location and on the same time frame. This is important to have data on the same location and same time periods, since forest fire mostly triggered by weather or temperature condition on certain area on the certain time. Since there are many parameters involved, the preprocessing will need to made the data have standard structure. The clustering process needed to give a label to the data with five class label, very low risk, low risk, medium risk, high risk, very high risk. Based on the clustered data, the data training and data test given for random forest classifier for model development, the composition of the data training and data test is 70:30. The accuracies of both algorithm is very good 100%, precision, recall and f1-score also have very high score 100%. This meant that the forest fire prediction model will produce a good prediction.

Kata Kunci

Forest Fire; Random Forest; Linear Regression; Prediction; Data Mining

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