Cluster Analysis Using K-Means Method to Classify Sumatera Regency and City Based on Human Development Index Indicator

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 variables including life expectancy at birth, school continuity, average of school continuity and expenditure per capita. In this study, we classify districts or cities on the island of Sumatra based on 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 divided into three Cluster. The first cluster or the middle area contained 41 cities. The second cluster or the high area contained 21 regencies/cities. The third cluster or the low area contained 92 regencies /cities. Areas with low scores are of more concern because all indicators are below the average value, these areas are like Pidie, Nias Utara, Pesisir Barat and etc.

Human Development Clustering in Indonesia: Using K-Means Method and Based on Human Development Index Categories

“…The algorithm of this method groups objects based on the distance between the objects and the centroid cluster [17] where the distance is obtained by an iterative process. The analysis needs to determine the number of k as input to the algorithm [12], [18]. The goal of K-Means clustering is to get clusters of objects by maximizing the similarity of objects within clusters (or minimizing variance within clusters) and maximizing the differences between clusters.…”

“…The researchers found that the number of clusters is four clusters using K-Means [4], [5], [10] and Fuzzy C-Means [10], but two clusters for hierarchical clustering and five clusters for indah.fahmiyah@ftmm.unair.ac.id K-Medoids [5]. HDI grouping in a particular area in Indonesia was also carried out [1], [11], [12]. Grouping human development of countries in the world is also carried out using the K-means and Partitioning Around Medoids algorithms and offers cut-off values to classify countries with low (lower than 65), medium (65-85), and high (greater than 85) human development [13].…”

“…This was also followed by Indonesia. These dimensions are long and healthy life, knowledge, and a decent standard of living [1], [2], [12], [14]. These three dimensions are the chosen approach in describing the quality of human life and have not changed until now.…”