Cluster Analysis Using Principal Component Analysis Method and K-Means to Find Out the Compliance Group of Property Tax

  • Rully Pramudita Bina Insani University
  • Nining Rahaningsih STMIK IKMI Cirebon
  • Sekar Puspita Arum Bina Insani University
  • Medina Aprilia Putri STMIK IKMI Cirebon
  • Sok Piseth National Polytechnic Instutite of Cambodia
Keywords: clustering, K-Means, machine learning, PCA, property tax




The village of Kendal has experienced a decline in local income due to the high rate of property tax arrears, with 226 taxpayers (19% of residents) known to have outstanding payments. Additionally, with 1,159 separate residents residing in 10 block areas with varying tax amounts, it has become increasingly difficult for the Village Apparatus to profile taxpayers based on their characteristics. To overcome these problems, a data analysis model based on Machine Learning technology will be developed using the Principal Component Analysis (PCA) Method combined with the K-Means method. The objective of this study is to create a cluster analysis model that can accurately map the characteristics of taxpayers, making it easier for the Village Apparatus to identify and assist residents who need to pay their property tax. This proposed solution will also simplify the reporting process to the central government regarding the estimated regional revenue sourced from property tax.


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