Implementation of K-means clustering method to distribution of high school teachers

Currently, the government is still having difficulties in distributing teachers. The current problem is not just about less teachers, but also more teachers in some cities. The problem of unequal distribution of teachers then became dependent on local government. The distribution of teachers now can not be centralized because of the decentralization system implemented in Indonesia. Clustering in data mining is useful for finding distribution patterns within a dataset that is useful for data analysis processes. Using clustering, identifiable densely populated areas, overall distribution patterns and attractive associations between data attributes. The purpose of this research is to apply ¿-means clustering algorithm to analyze distribution of high school teachers in Indonesia. This research uses three steps, namely dataset selection, preprocessing data, and application of k-means clustering. Testing is done by using k cluster, that is k = 12. The cluster results are analyzed to classify clusters into 3 categories, namely less, enough, and more teachers. Testing results obtained data Sum of Squared Error (SSE) with percentage 87.15%. While the clustering results produce clusters 3 and 5 in the category of less teachers. Cluster 1 and 9 in the category of enough teachers. While cluster 2,4,6,7,8,10,11,12 in the category of more teachers. Based on the results obtained it can be concluded that the accuracy of the algorithm used with 12 clusters is very high. The results of this clustering analysis can also be used as a reference for the distribution of teachers to region with less teachers, so as to solve the issue of uneven distribution of teachers.