| dc.description.abstract | Human Development Index (HDI) is an indicator commonly used by the United Nations Development Programme (UNDP) to assess the success of development. Three elements forming the HDI value are the level of health, education, and economics. The HDI data per district used in this research only cover Java region in 2012, obtained from the Central Bureau of Statistics including spatial data (area coordinates), temporal data (years), and non-spatial data (HDI). Data mining technique applied in this research is clustering with ST-DBSCAN using Python programming language. Clustering can be used to found similar grouping based on HDI in each region. Visualization of HDI data objects produces a map of the cluster spread. By using spatial distance parameter (Eps1)=0.4, temporal distance (Eps2)=0, size of the tolerated difference in value of non-spatial atributes (Δ)=2, and minimum number of members in each cluster (MinPts)=5 resulted in 4 clusters and 18 point of noise with silhouette coefficient worth 0.020. | en |
