Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau
Abstract: Over one-year period
(March 2013-March 2014), 58 percent of all detected hotspots in Indonesia are
found in Riau Province. According to the data, Rokan Hilir shared the greatest
number of hotspots, about 75% hotspots alert occur in peatland areas. This
study applied spatial decision tree algorithms to classify classes before
burned, burned and after burned from remote sensed data of peatland area in
Kubu and Pasir Limau Kapas subdistrict, Rokan Hilir, Riau. The decision tree
algorithm based on spatial autocorrelation is applied by involving Neigborhood
Split Autocorrelation Ratio (NSAR) to the information gain of CART algorithm.
This spatial decision tree classification method is compared to the
conventional decision tree algorithms, namely, Classification and Regression
Trees (CART), C5.0, and C4.5 algorithm. The experimental results showed that
the C5.0 algorithm generate the most accurate classifier with the accuracy of
99.79%. The implementation of spatial decision tree algorithm successfully
improves the accuracy of CART algorithm.
Author: Putri Thariqa
Journal Code: jptkomputergg160295
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