Clustering Tax Compliance Data Using <i>k</i>-Means Algorithm: A Study Case of Manufacturing Companies in Indonesia

This research examines the use of machine learning to group a collection of data related to the tax compliance of manufacturing firms in the Greater Jakarta area of Indonesia. The data set was obtained through a survey and was able to collect data from 209 respondents who represented the finance department of the companies. The k -means algorithm is applied to develop machine learning. The clustering aims at dividing the data set on the basis of similarity into three clusters. The result showed that the machine learning model was able to cluster the data into three groups. An evaluation was presented by comparing the clustering result with a classification result based on the average survey score that has been studied previously. The evaluation shows a small correlation between the clusters and the average survey score. Compliance of tax payers is a complex system and cannot be merely indicated based in the survey score. The clustering technique demonstrated its usefulness in uncovering intriguing patterns, distributions, and the fundamental structure of the data.