ANALISIS HIERARCHICAL CLUSTERING MULTISCALE BOOTSTRAP (KASUS: INDIKATOR KEMISKINAN DI PROVINSI SULAWESI SELATAN TAHUN 2020) - Musdalifah M. Ramly; Sudarmin Sudarmin; Bobby Poerwanto (VARIANSI: Journal of Statistics and Its application on Teaching and Research, 2022)
Hierarchical cluster analysis is a statistical analysis used to group data based on their similarities. The single linkage, complete linkage and average linkage methods can be used to group data using distance techniques. There is a large difference in the number of poor people in urban and rural areas in South Sulawesi Province, so an analysis is needed to classify areas that have the same characteristics based on poverty indicators. For this reason, these three methods are used. However, the results of this analysis are only based on the similarity measure based on the distance technique used. Thus, the multiscale bootstrap method is used to obtain the validity of the resulting clusters. The results of the research using these three methods are four clusters with different characteristics. By using multiscale bootstrap, it is found that in single linkage there are four valid clusters, for complete linkage there is only one valid cluster and on average linkage there are three valid clusters. So it is found that single linkage is the best method in classifying these cases.