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Penerapan Algoritma K-Means Dan K-Medoids Clustering Untuk Mengelompokkan Tindak Kriminalitas Berdasarkan Provinsi


Dedi Suhendro, S.E., M.Si, (2021) Penerapan Algoritma K-Means Dan K-Medoids Clustering Untuk Mengelompokkan Tindak Kriminalitas Berdasarkan Provinsi, Jurnal Penelitian ,Universitas Harapan Bangsa
Jurnal Penelitian
Dedi Suhendro, S.E., M.Si, | pdf
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Abstrak
Crime is a problem that often occurs in everyday life and everywhere, including in various provinces in Indonesia. In this study, criminal acts will be grouped using the K-Means and K-Medoids clustering algorithms. The difference in the number of clusters in the performance of each algorithm has a different calculation pattern in each iteration depending on the dataset used and the centroid point that is used as a calculation in the algorithm. The data is processed into two clusters, namely
the high crime rate cluster (C1) and the crime rate cluster. low crime (C2). The results of the K-Means algorithm show that C1 has 6 members and C2 has 28 members. While the results of the manual calculation of the K-Medoids algorithm obtained the results of C1 having 7 members and C2 having 27 members