Penerapan Data Mining Menggunakan Metode Clustering Untuk Mengetahui Kelompok Kepatuhan Wajib Pajak Bumi dan Bangunan


  • Medina Aprilia Putri STMIK IKMI Cirebon
  • Nining Rahaningsih STMIK IKMI Cirebon
  • Fadhil M. Basysyar STMIK IKMI Cirebon
  • Odi Nurdiawan STMIK IKMI Cirebon



Clustering, Data Mining, K-Means, Kepatuhan Pajak, Pajak Bumi dan Bangunan


Local taxes are taxes set by local governments whose collection authority and tax proceeds are used to fund regional expenditures. One of the taxes included in the authority of local governments is the Land and Building Tax (PBB). Land and Building Tax is one of the taxes that can be paid through the village government. With the increasing number of taxpayers in the village, the data on payment of tax contributions that go directly to the state treasury causes the Kendal Village government, Astanajapura District, Cirebon Regency not to know how many taxpayers are obedient and disobedient. This study uses data mining techniques namely the Clustering Method using the K-Means method. This study uses the Knowledge Discovery in Database (KDD) stage with the amount of data used as much as 1,159 in the form of taxpayer data for the Kendal Village community in 2021. The results of the RapidMiner test using the Davies Bouldin Index calculation obtained a cluster determination value with a value of 4 (0.862). Cluster 0 contains members who have a low level of compliance in paying PBB, Cluster 1 contains members of taxpayers with a moderate level of compliance in paying PBB, Cluster 2 has a high level of taxpayer compliance and Cluster 3 is a cluster with a very high level of taxpayer compliance. By having the most dominant average price determined by PBB in each cluster is Rp. 18.000,-.