Penerapan K-Means Clustering dalam Pengelompokan Pendapatan Pajak Kendaraan Bermotor di Jawa Barat

Abstract

Motorized vehicles are now the fastest growing type of transportation due to their wide use and accessibility and are also the main source of tax revenue. This research uses a dataset from the West Java Province Bapenda on motor vehicle taxes (PKB) for the 2013-2023 period and this data is based on consistently publishes the amount of PKB Principal income and PKB Fines every year. The clustering process starts from the initial selection stage to the evaluation stage, which is carried out using Google Colab with the Python programming language. Some libraries used in this analysis include numpy, pandas, matplotlib, plotly express, and scikit-learn. The results showed that there were 5 PKB in cluster 0, 9 PKB in cluster 1, 11 PKB in cluster 2, 8 PKB in cluster 3, and 5 PKB in cluster 4.