Abstract

Motor vehicle taxes play a significant role in generating revenue for local governments, making it imperative to prioritize the collection of these taxes. In order to achieve the predetermined targets for motor vehicle tax revenue, it becomes necessary to closely monitor taxpayers who fail to comply with their tax payment obligations. This study focuses on classifying taxpayers into three distinct categories using the Objects and Subjects Data Collection Letter (SPOS), Tax Calculation Note (NPP), and Tax Bill Note (NTP) as classification criteria. To compare the performance of different Machine Learning algorithms, the classification process is conducted. The evaluation of algorithm performance is carried out using the 10 Folds Cross Validation method with a data split of 60:40, and metrics such as Accuracy, Precision, Recall, and F1-score are employed to assess algorithm effectiveness.

Original languageEnglish
Title of host publication2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-264
Number of pages4
ISBN (Electronic)9798350312164
DOIs
Publication statusPublished - 2023
Event14th International Conference on Information and Communication Technology and System, ICTS 2023 - Surabaya, Indonesia
Duration: 4 Oct 20235 Oct 2023

Publication series

Name2023 14th International Conference on Information and Communication Technology and System, ICTS 2023

Conference

Conference14th International Conference on Information and Communication Technology and System, ICTS 2023
Country/TerritoryIndonesia
CitySurabaya
Period4/10/235/10/23

Keywords

  • classification
  • machine learning
  • motor vehicle tax
  • taxpayer noncompliance

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