Clustering Profil Pengunjung Perpustakaan Menggunakan Algoritma K-Means
(Studi Kasus Perpustakaan BP Batam)
Abstract
Business Entity library Batam (BP Batam) is a public library located in Batam city with thw number of visitors. Every visitor who comes to do the charging guest book manually by writing system. It causes a buildup of data which are not organized. Data mining is one of the analytical tools that can be used to address the backlog of data. The method of Clustering with the K-Means Algorithm used in analyzing the data library visitors BP Batam. Library visitors using the data processing method of Elbow to get the best number of clusters K i.e., K = 3, and by using the center point (centroid) initial i,e, P1 = (4,1), P2 = (2,4), P3 = (4,2). The purpose of this research is to apply the algorithm for K-Means clustering in the data library visitors (case study library BP Batam). K-Means clustering results obtained from 1556 dataset data library visitors are grouped into three clusters, Clusters 1 is dominated by a college student and visitor located at Batam Center, Cluster 2 is dominated by a college student and visitor located at Bengkong, Cluster 3 is dominated by public and visitor status in Batam Center.
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