Abstract
The Human Development Index (abbreviated as HDI) is an indicator used by a country to measure success in efforts to develop human quality. The purpose of this research is to make HDI mapping in areas in Indonesia by utilizing data mining techniques. Source of data used comes from official data from the Indonesian Central Statistics Agency (https://www.bps.go.id/) consisting of 34 data records (2018-2019). Indicators used in mapping are Life Expectancy at Birth (X1), Expectations of Old School (X2) and Average Length of School (X3). The data mining technique used is part of clustering, namely K-Medoids. The analysis process uses the help of RapidMiner software 5.3. Determination of the number of clusters (k) in the mapping using the Davies Bouldin (DBI) parameter with a maximum value (k = 4) = 0.856. By using four mapping labels (C1 = "very high" group; C2 = "high" group; C3 = "medium" group; C4 = "low" group), the results of C1 = 5 province; C2 = 16 provinces; C3 = 10 provinces and C4 = 3 provinces. Based on the results of the mapping of regions in Indonesia, Indonesia's HDI is still far behind when compared to countries in ASEAN. In the future, this will be submitted to the government to make HDI a priority because it involves the welfare and quality of Indonesian people.
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