Taxpayer Awareness Classification Using Decision Tree and Naïve Bayes Methods
Abstract
Land and Building Tax (PBB) has a big influence on a region's PAD. Therefore, regions always strive to increase PBB income as much as possible. Many factors influence the increase in PBB, one of which is public awareness of taxes. Lack of public awareness of taxes causes PBB income to also decrease, and has implications for regional PAD. And conversely, if public awareness of taxes is high, PBB and PAD revenues will also increase. Therefore, a system is needed to measure public awareness of taxes in the region. If public awareness of taxes can be measured, then the relevant agencies can evaluate and map taxpayers in which sub-districts have high or below average awareness. There are several factors that influence taxpayer awareness, including ownership status, tax sector, assessment category, and the number of receivable payments over the past 5 years. By knowing the awareness of taxpayers, the relevant agencies can review the targets for achieving PBB revenue and issue warning letters to taxpayers whose awareness of PBB is lacking. This research uses decision tree and naïve Bayes methods to classify 666,580 datasets obtained from the Cianjur Regency Regional Revenue Management Agency. The stages are carried out by data collection, data preprocessing, training data labeling, classification process, and evaluation. The result of this research is a system that can predict whether taxpayers are aware or not in a sub-district and sub-district or rural area using decision trees and naïve Bayes. The accuracy obtained from the decision tree method was 93.73%, while the accuracy obtained from the naïve Bayes method was 85.61%.
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