  Determination quadrant development has an important role in order to
determine the achievement of the development of a district, in terms of the
sector's gross regional domestic product (GDP). The process of determining the
quadrant development typically uses Klassen rules based on its sector GDP. This
study aims to provide a new approach in the conduct of regional development
quadrant clustering using cluster techniques. Clustering is performed based on
the average value of the growth and development of a district contribution
compared with the average value and contribution of the development of the
province based on data in comparison with a year of data to be compared.
Testing models of clustering, performed on a dataset of two provinces, namely
Banten (as a data testing) and Central Java (as the training data), to see the
accuracy of the classification model proposed. The proposed model consists of
two learning methods in it, namely unsupervised (Self Organizing Map / SOM-NN)
method and supervised (Naive Bayess). SOM-NN method is used as a learning
engine to generate training data for the target Class that will be used in the
machine learning Naive Bayess. The results showed the clustering accuracy rate
of the model was 98.1%, while the clustering accuracy rate of the model results
compared to manual analysis shows the accuracy of the typology Klassen smaller,
ie 29.63%. On one side, clustering results of the proposed model is influenced
by the number and keagaraman data sets used.
