PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES

  • Andrew Donda Munthe Badan Pusat Statistik (BPS)
  • I Made Sumertajaya Department of Statistics, IPB
  • Utami Dyah Syafitri Department of Statistics, IPB
Keywords: clustering, K-prototype algorithm, two step cluster, villages

Abstract

Statistic Indonesia (BPS) noted that in 2014 there were 3.270 villages in Nusa Tenggara Timur Province. Most of them have a high percentage of poverty. Therefore, the village clustering based on poverty indicators is very important. The clustering algorithm that can be used on large data size and with mixed variables are Two Step Cluster (TSC) and K-Prototypes. The purpose of this research is to compare of TSC and K-Prototypes algorithm for village clustering in Nusa Tenggara Timur Province based on poverty indicators. The data were taken from 2014 village potential data (PODES 2014) collected by BPS. The best selection criteria for the cluster is the minimum ratio between variance within groups and variance between groups. The result showed that the best clustering algorithm was TSC which had the smallest ratio (2.6963). The best clustering showed that villages in Nusa Tenggara Timur Province divided into six groups with different characteristics.

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Published
2018-11-30
Section
Articles