Muhammad Faishal Jundana Muttaqin
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.
- McGillivray, M., & White, H. (1993). Measuring development? The UNDP's human development index. Journal of international development, 5(2), 183--192.Google Scholar
Cross Ref
- Sagar, A. D., & Najam, A. (1998). The human development index: a critical review. Ecological economics, 25(3), 249--264.Google Scholar
- Statistik, B. P. (2019). Indeks Pembangunan Manusia 2018. Jakarta (ID): Badan Pusat Statistik.Google Scholar
- Yang, Y., & Hu, A. (2008). Investigating regional disparities of China's human development with cluster analysis: A historical perspective. Social Indicators Research, 86(3), 417--432.Google ScholarCross Ref
- Shah, I., & Gosavi, V. (2010). Human Development Index and Human Poverty Index for Indian states, 2005: Multivariate Statistical Analysis of basic indicators.Google Scholar
- Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100--108.Google ScholarCross Ref
- Ningrat, D. R., Di Asih, I. M., & Wuryandari, T. (2016). Analisis Cluster Dengan Algoritma K-Means Dan Fuzzy C-Means Clustering Untuk Pengelompokan Data Obligasi Korporasi. Jurnal Gaussian, 5(4), 641--650.Google Scholar
- Ali, S. B., Mahdi, A., & Malihe, J. (2012). The effect of employees' performance appraisal procedure on their intrinsic motivation. International Journal of Academic Research in Business and Social Sciences, 2(12), 161.Google Scholar
Index Terms
- Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator
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