<![CDATA[Developing hotspot prediction models using decision tree algorithms require target classes to which objects in a dataset are classified. In modeling hotspots occurrence, target classes are the true class representing hotspots occurrence and the false class indicating non hotspots occurrence. This paper presents the results of satellite image processing in order to determine the radius of a hotspot such that random points are generated outside a hotspot buffer as false alarm data. Clustering and majority filtering were performed on the Landsat TM image to extract burn scars in the study area i.e. Rokan Hilir, Riau Province Indonesia. Calculation on burn areas and FIRMS MODIS fire/hotspots in 2006 results the radius of a hotspot 0.90737 km. Therefore, non-hotspots were randomly generated in areas that are located 0.90737 km away from a hotspot. Three decision tree algorithms i.e. ID3, C4.5 and extended spatial ID3 have been applied on a dataset containing 235 objects that have the true class and 326 objects that have the false class. The results are decision trees for modeling hotspots occurrence which have the accuracy of 49.02% for the ID3 decision tree, 65.24% for the C4.5 decision tree, and 71.66% for the extended spatial ID3 decision tree.]]>
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