Spatial Classification of Forest and Land Fire Risk using Decision Tree C5.0 Algorithm

Abstract

Forest and land fires in indonesia occur almost every year, one of the districts that often experience forest and land fires is Ogan Komering Ilir District, South Sumatra Province. One indicator of forest and land fires is hotspots. This study aims to create a classification model for the forest and land fire risk in Ogan Komering Ilir District using four attributes, namely hotspot density, distance to rivers, distance to settlements, and distance to roads. The decision tree C5.0 algorithm is used to develop a classification model. The decision tree C5.0 produces a model that can be used to classify new data based on the rules formed by the tree. The results of modeling using C5.0 with 80% training data and 20% test data show that the performance of the model can correctly classify around 86.49% of the total sample in the data. This shows that the model has a relatively high level of accuracy in classifying. The modeling decision tree shows that forest and land fires are most affected by distance to settlements