Penerapan Metode Complete Linkage dan Metode Hierarchical Clustering Multiscale Bootstrap - Lisda Ramadhani; Ika Purnamasari; Fidia Deny Tisna Amijaya (Program Studi Statistika FMIPA Universitas Mulawarman, 2018)
Cluster analysis is an analysis that has a purpose to grouping the data (object). The multiscale bootstrap method in cluster analysis is used as a manner for looking at the validity from the result of cluster analysis. The working process of multiscale bootstrap in cluster analysis is taking a sample that has been bootstrapped and then take the one of bootstrap resampling result that has been reputed to represent the distribution in East Kalimantan 2016. The purpose of this research is looking at the result of data agglomeration in poverty indicator in East Kalimantan 2016 in using a multiscale bootstrap method that produces four cluster types. The first cluster consists of two regencies/cities who has the low percentage of poverty indicator 49,32%. Additionally, the second cluster contains of five regencies/cities with the high percentage of poverty indicator 53,39%. In addition, the third cluster involves of two regencies/cities with the percentage of poverty indicator in high sufficient 51,46%. Finally, the fourth cluster consists of a regency/city which has a percentage of poverty indicator low adequate 51,02%.