Classification of Human Resources Readiness in Asia: K-Means Clustering Using R Studio

Abstract

This research aims to classify dichotomously the differences and diversity in human resources of all countries in the Asian region to clarify the position of each country based on the state of its human resources. Several countries were not included in the analysis due to limited data availability. This research uses the K-Mean clustering method. Clustering is an approach to dividing a set of points into similar groups called clusters. K-Mean clustering is one of the most popular unsupervised learning methods in machine learning. Based on the results of the K-Mean clustering analysis, the readiness of human resources in Asian countries shows that there are clusters or groups, with cluster 1 having as many as 29 members and cluster 2 having as many as 9 members. K-Mean clustering for the optimal number of two clusters is dichotomous, so it uses clustering assumptions between country clusters with good and poor human resource readiness.