Sistem Informasi Pemetaan Pendidikan Menggunakan Algoritma Data Mining

Abstract

in this study to identify the increase in educational services based on the quality of non-formal education is an indicator, having tiered in terms of education, non-formal education (training) to be one of the prerequisites in multiplying the potential for self-development. Data mining algorithms is a basic k-means clustering to put the object based on the average (Means) nearest cluster. Aims to design mapping information system education with the k-means cluster. Application k-means cluster is part of a non-hierarchical method, the mapping system of education in 171 samples of data Isalam Students Association (HMI) were tested in this study showed that the non-hierarchical method (k-means cluster) has a good degree of accuracy because they specify the number of clusters in advance. Education information system mapping is used to cluster the data level, corresponding formal education and training has been followed. Education information system mapping is used to cluster the data level, corresponding formal education and training has been followed . The test results have in me some real, the spread of the data in each cluster are similar.  At the time of  the iteration process is not visible difference in the results of the mapping study using the k-means cluster. Results of a cluster centroid information models with variable 4 educated members include S1, S2, has entered basic training cluster  0, educated S1, S2, S3 has entered basic training cluster 1, S1 has educated basic training and training of incoming intermediate cluster 2, educated S1 has entered basic training cluster 3. formal education, education tiered seen in cluster 1 for non-formal education  (training) tiered education seen in cluster 2. Based the test results k-means cluster.

Keywords: Information Systems; Educational Mapping; Cluster; K–means