Analisis Perbandingan K-Means dan K-Medoids dalam Pengelompokan Provinsi Berdasarkan Indeks Demokrasi Indonesia 2021 - Rudianto, Regita Dewanti; Wijayanto, Arie Wahyu (Computer Engineering Departement, Universitas Komputer Indonesia, 2024)
The clustering method is one method in data mining and is useful in grouping observations that do not have a target / class. One of the analyses that can be done from this clustering is the grouping of 34 provinces in Indonesia based on aspects in the 2021 Indonesian Democracy Index (IDI). The aspects of the IDI include the Freedom Aspect, Equality Aspect, and the Capacity Aspect of Democratic Institutions. Clustering analysis needs to be done to determine the grouping of IDI aspects and their characteristics. The clustering methods used in this study are K-Means and K-Medoids. For the selection of the optimal number of clusters used Dunn Index, Silhouette Index, Calinski-Harabasz Index and Davies-Bouldin Index. To obtain the best model, a comparison is made using the ratio between average within (Sw) and average between (Sb). The results obtained are that there are 5 clusters in the IDI grouping using the K-Medoids algorithm because the ratio of Sw/Sb is smaller than K-Means. With this grouping, it is hoped that the government and related parties can utilize the results of this analysis in formulating policies and maintaining political stability in Indonesia.