BPJS SERVICE DATA CLUSTERIZATION USING K-MEANS ALGORITHM
(Case Study: BPJS Binjai Office)
Keywords:
Pelayanan BPJS, Data Mining, K-Means.Abstract
Employment BPJS is a program formed by the government to provide social protection to workers. Due to the large number of workers, for example, using the Death Insurance program (JKM), it will produce abundant and accumulating data. To find out BPJS service data is to group BPJS service data in BPJS. One of the most widely used methods in the clustering method is to use the K-Means algorithm. K-Means is a non-hierarchical (block) grouping method that seeks to partition data into clusters/groups so that data with the same characteristics will be included in the clustering method. in the same cluster and data with different characteristics are grouped into another group. From the 20 data obtained 3 groups, Cluster 1 has 3 data, Cluster 2 has 4 data, and Cluster 3 has 13 data. Cluster 1 has the male sex who has the BPJS Old Age Guarantee (JHT) program which gets class III services. Cluster 2 has the male sex who has the BPJS Death Insurance (JKM) program who gets class I services. cluster 3 there are women who have the BPJS Death Guarantee program (JKM) who get class II types of services.
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