PERBANDINGAN ALGORITMA K-MEANS DENGAN K-MEDOIDS UNTUK PENGELOMPOKKAN TINGKAT KRIMINALITAS DI INDONESIA TAHUN 2020

Muhammad Ulil Fahmi, NIM.: 19106010045 (2023) PERBANDINGAN ALGORITMA K-MEANS DENGAN K-MEDOIDS UNTUK PENGELOMPOKKAN TINGKAT KRIMINALITAS DI INDONESIA TAHUN 2020. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Cluster analysis is a technique for summarizing data, namely by grouping objects according to the characteristics of each object. The K-Means is a distance-based clustering method which divides data into a number of clusters. K-Medoids method is a clustering method where this method overcomes the weaknesses of K-Means. The second method is used to classify crime data in Indonesia in 2020. In this study, the Davies Bouldin Index was used to determine the best method for data on crime in Indonesia in 2020. The Davies Bouldin Index (DBI) can be interpreted as the smaller the DBI value, the better the cluster obtained. The results of this study indicate that three clusters are formed where the K-Means no outlier method produces a DBI value of 0,9916 which is smaller than the DBI value for K-Medoids no outlier of 1,8733. So in this case, the K-Means no outlier method is a better method than the K-Medoids no outlier method.

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