Algoritma K-Means untuk Mengelompokkan Tingkat Pengangguran Terbuka (TPT) menurut Provinsi di Indonesia

Abstract

Unemployment is one of the main problems faced by many countries, including Indonesia. The Open Unemployment Rate (OER) is an important indicator used to measure the amount of labor force that is not absorbed in the labor market. This research aims to Cluster the provinces in Indonesia based on unemployment rate and school enrollment rate, so as to provide a clearer picture of the distribution of unemployment in different regions: The study identified three main Clusters: Cluster 1: Provinces with high unemployment rates. Cluster 2: Provinces with a medium unemployment rate. Cluster 3: Provinces with low unemployment rates. Distribution: The Clustering results show that 13 provinces are included in Cluster 1, 18 provinces in Cluster 2, and 3 provinces in Cluster 3. This study found that the K-Means algorithm is effective in Clustering provinces based on TPT and school enrollment rates. The Clustering results show significant variation between provinces, with some provinces having higher unemployment rates and lower school enrollment than others.This study successfully Clustered Indonesian provinces based on unemployment and school enrollment rates using the K-Means algorithm. The Clustering results provide valuable insights into the distribution of unemployment in Indonesia and can be used as a basis for more effective policy making.