Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator

Muhammad Faishal Jundana Muttaqin, Zulkarnain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Human development progress in Indonesia is characterized by the increasing score of human development index (HDI). HDI is an important indicator in measuring efforts to build the quality and equity of human life. HDI consists of four factors including life expectancy at birth, school continuity, average of school continuity and expenditure per capita. In this research we classify Indonesia regency or city based on the HDI into three categories; high, middle, and low area. We use cluster analysis for the research. Cluster analysis is a class of multivariate techniques that are used to classify objects or cases into relative groups called clusters. One of the cluster analysis methods is k-means. The result of this research are divided into three groups; high area, medium area and low area. The first group or the low area contained 19 cities. The second group or the middle area contained 381 regencies/cities. The third group or the high area contained 114 regencies /cities.

Original languageEnglish
Title of host publicationAsia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020 - Proceedings
PublisherAssociation for Computing Machinery
Pages81-85
Number of pages5
ISBN (Electronic)9781450376006
DOIs
Publication statusPublished - 16 Jun 2020
Event3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020 - Depok, Online, Indonesia
Duration: 16 Jun 2020 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020
Country/TerritoryIndonesia
CityDepok, Online
Period16/06/20 → …

Keywords

  • Cluster Analysis
  • Human Development Index
  • K-Means

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