Hierarchic Cluster Analysis of the Distribution of Covid-19 Cases by Province in Indonesia

EasyChair Preprint 9192

Abstract

This study aims to apply Hierarchical cluster analysis to the distribution of Covid-19 cases by province in Indonesia. The data used is secondary data for the 2021 period. The variables used are the number of confirmed patients, the number of recovered patients, the number of patients who died, the population, population density, the number of elderly people, and health facilities. The method in this research is hierarchical cluster analysis with agglomeration process, Beetween groups linkage, with the concept of square Euclidean distance. The research step begins with data exploration, standardization, multicollinearity assumption test and Barlett KMO test, hierarchical cluster analysis, and interpretation. The results showed that the clustering process in Hierarchical analysis can be determined based on the desired number of clusters and the results are strengthened by the Dendogram representation. The distribution of Covid-19 cases in 34 provinces is divided into 2,3, and 4 clusters based on the variables used. Prov. DKI Jakarta has different characteristics from the prov. West Java, Central Java, East Java. While the province. Banten sd. Prov. Gorontalo has similar characteristics in the case of the spread of Covid-19.

Keyword : Covid-19, analsis cluster, K-Means cluster

Keyphrases: COVID-19, K-means Cluster Analysis, cluster analysis

@booklet{EasyChair:9192,
  author    = {Euis Sartika and Sri Murniati and Agus Binarto},
  title     = {Hierarchic Cluster Analysis of the Distribution of Covid-19 Cases by Province in Indonesia},
  howpublished = {EasyChair Preprint 9192},
  year      = {EasyChair, 2022}}