K-Means Clustering Algorithm: A Study on Unemployment Rates in Districts/Cities in Three Highest Provinces

  • Mohammad Dian Purnama State University of Surabaya
  • Mutia Eva Mustafidah State University of Surabaya
Keywords: K-Means, Clustering, Unemployment


Unemployment is a recurring issue every year, particularly in provinces with high unemployment rates, posing economic and social challenges. West Java, Riau Islands, and Banten are identified as the three provinces with the highest unemployment rates, exceeding 8% in the year 2022. Hence, this study aims to delve into the unemployment scenario in these provinces, considering various influencing factors drawn from relevant previous research. The primary objective of this research is to obtain the classification results of regencies/cities in West Java, Riau Islands, and Banten based on unemployment indicators. The findings reveal four clusters: Cluster 1 comprises 13 regencies/cities with the lowest unemployment rates, Cluster 2 includes 4 regencies/cities with low unemployment rates, Cluster 3 consists of 13 regencies/cities with moderate unemployment rates, and Cluster 4 encompasses 12 regencies/cities with high unemployment rates.