  Regional development classification is one way to look at differences in
levels of development outcomes. Some frequently used methods are the shift
share, Gain index, the Iindex Williamson and Klassen typology. The development
of science in the field of data mining, offers a new way for regional
development data classification. This study discusses how the decision tree is
used to classify the level of development based on indicators of regional gross
domestic product (GDP). GDP Data Central Java and Banten used in this study.
Before the data is entered into the decision tree forming algorithm, both the
provincial GDP data are classified using Klassen typology. Three decision tree
algorithms, namely J48, NBTRee and REPTree tested in this study using
cross-validation evaluation, then selected one of the best performing
algorithms. The results show that the J48 has a better accuracy rate which is
equal to 85.18% compared to the algorithm NBTRee and REPTree. Testing the model
is done to the six districts / municipalities in the province of Banten, and
shows that there are two districts / cities are still at the development of the
status quadrant relatively underdeveloped regions, namely Kota Tangerang and
Kabupaten Tangerang. As for the Central Java Province, Kendal, Magelang,
Pemalang, Rembang, Semarang and Wonosobo are an area with a quadrant of
development also on the status of the region is relatively underdeveloped.
Classification model that has been developed is able to classify the level of
development fast and easy to enter data directly into the decision tree is
formed. This study can be used as an alternative decision support for policy
makers in order to determine the future direction of development.
