Comparison of DBSCAN and K-Means Clustering for Grouping the Village Status in Central Java 2020

Cesaria Dewi; Emban Permata  Siam; Gona Asri  Wijayanti; Mustika  Putri; Nurfitri  Aulia; Rani  Nooraeni

doi:10.20956/j.v17i3.11704

Authors

Cesaria Dewi Politeknik Statistika STIS
Emban Permata Siam Politeknik Statistika STIS
Gona Asri Wijayanti Politeknik Statistika STIS
Mustika Putri Politeknik Statistika STIS
Nurfitri Aulia Politeknik Statistika STIS
Rani Nooraeni Politeknik Statistika STIS

DOI:

https://doi.org/10.20956/j.v17i3.11704

Keywords:

Keywords: Village Status, DBSCAN clustering, K-Means clustering, Covid-19

Abstract

Abstract

Since Covid-19 was declared as a pandemic disaster, the world economic order has begun to be shaken, and Indonesia is no exception. Indonesia's economic growth has continued to contract since quarter II. Central Java Province is in the third place with the highest number of positive cases in Indonesia. The government try to improve the quality control over the implementation of village funds by observing the classification of village status. The status has been made by the Ministry of Villages based on IDM value. The purpose of this study is to create a village status cluster based on the three index values that compose the IDM, namely IKS, IKL, and IKE. This goal is realized through a comparative analysis of two clustering methods, that is K-means and DBSCAN. The results showed that by using the DBSCAN 4 clusters were formed, while using the K-means 3 clusters were formed. The silhouette value for each cluster formed using the DBSCAN is higher than the silhouette of clusters formed by the K-means and it is concluded that the DBSCAN is more appropriate to use in clustering village status in Central Java province in 2020 than K-means.

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