Penerapan Data Mining Menggunakan Teknik Classification Untuk Melihat Potensi Kepatuhan Wajib Pajak Badan
DOI:
https://doi.org/10.30865/jurikom.v13i1.9354Keywords:
Data Mining, Classification, Pajak Badan, Naive Bayes, Taxpayer Compliance, Corporate TaxAbstract
The application of data mining using classification techniques has significant potential to assist tax authorities in identifying and mapping the compliance levels of corporate taxpayers. This study aims to develop a corporate taxpayer compliance classification model using the Naive Bayes algorithm based on the ratio of Annual Tax Return (SPT) filing and the ratio of tax payments. The data used consist of aggregated data from Tax Service Offices (Kantor Pelayanan Pajak/KPP) for the 2022–2024 period obtained from the Directorate General of Taxes. The research stages follow the Knowledge Discovery in Databases (KDD) methodology, which includes data selection, preprocessing, transformation, modeling, and evaluation. The experimental results indicate that the Naive Bayes model is able to classify compliance levels with an accuracy of 100%, precision of 1.00, recall of 1.00, and an F1-score of 1.00. These findings suggest that the SPT filing ratio is the dominant factor in determining corporate taxpayer compliance. The proposed model can be utilized as a decision support system to assist tax authorities in determining supervision and guidance priorities for corporate taxpayers
References
[1] “THE ROLE OF TAXES IN THE DEVELOPMENT OF THE NATIONAL ECONOMY,” International journal of artificial intelligence, vol. 5, no. 02 SE-Articles, pp. 1101–1103, Feb. 2025, [Online]. Available: https://www.academicpublishers.org/journals/index.php/ijai/article/view/3001
[2] H. T. Nguyen and S. N. A. C. Darsono, “The Impacts of Tax Revenue and Investment on the Economic Growth in Southeast Asian Countries,” Journal of Accounting and Investment, vol. 23, no. 1 SE-Articles, pp. 128–146, Jan. 2022, doi: 10.18196/jai.v23i1.13270.
[3] I. A. Maulana and A. P. W. Wibowo, “Analysis of Public Sentiment Text Clustering on Tax Increases using Orange Data Mining on Twitter,” Brilliance: Research of Artificial Intelligence, vol. 5, no. 1 SE-Articles, pp. 93–99, Apr. 2025, doi: 10.47709/brilliance.v5i1.5787.
[4] K. Azmi and A. Evioni, “Application of Data Mining Using Classification for Potential Tax Costs,” Journal of Computer Science and Informatics Engineering , vol. 4, no. 1 SE-Articles, pp. 40–48, Jan. 2025, doi: 10.55537/cosie.v4i1.990.
[5] Y. G. Winata and M. Hadi, “Application of data mining techniques for VAT-Registered Business compliance,” Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia, vol. 4, no. 2 SE-Articles, pp. 243–260, Apr. 2023, doi: 10.52869/st.v4i2.317.
[6] Y. G. Winata, F. Noor, M. F. Bahar, A. B. Santoso, and E. Sukarno, “Application of data mining to taxpayers issuing fictitious tax invoice using classification techniques,” Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia, vol. 5, no. 1 SE-Articles, pp. 58–73, Oct. 2023, doi: 10.52869/st.v5i1.573.
[7] M. A. Putri, N. Rahaningsih, F. M. Basysyar, and O. Nurdiawan, “Penerapan Data Mining Menggunakan Metode Clustering Untuk Mengetahui Kelompok Kepatuhan Wajib Pajak Bumi dan Bangunan,” vol. 5, no. 2, pp. 145–156, 2022.
[8] A. Ginanjar and A. S. Wibowo, “Data Mining to Detect Fraud Patterns in a Taxpayer’s Financial Statement,” Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia, vol. 6, no. 2 SE-Articles, pp. 135–150, Apr. 2025, doi: 10.52869/st.v6i2.571.
[9] I. Vol, N. O. Issn, and N. P. Rahmayanti, “Research in Business & Social Science Effect of tax penalties , tax audit , and taxpayers awareness on corporate taxpayers ’ compliance moderated by compliance intentions,” vol. 9, no. 2, pp. 118–124, 2020.
[10] A. Putera Utama Siahaan, S. Sutiono, S. Pranoto, and R. Sri Mentari, “Analysis of Property Tax Payment Compliance Classification in Tebing Tinggi City Using the C4.5 Decision Tree Algorithm,” Journal of Information Technology, computer science and Electrical Engineering (JITCSE), vol. 1, no. 2, pp. 134–138, 2024, doi: 10.61306/jitcse.v1i2.
[11] B. De La Iglesia, “A Methodology: The KDD Roadmap.”
[12] A. Susanto and ; Meiryani, “Functions, Processes, Stages And Application Of Data Mining,” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, vol. 8, p. 7, 2019, [Online]. Available: www.ijstr.org
[13] V. Plotnikova, M. Dumas, and F. Milani, “Adaptations of data mining methodologies: A systematic literature review,” PeerJ Comput. Sci., vol. 6, pp. 1–43, 2020, doi: 10.7717/PEERJ-CS.267.
[14] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases) (© AAAI),” 1996. [Online]. Available: www.ffly.com/
[15] G. Mariscal, Ó. Marbán, and C. Fernández, “A survey of data mining and knowledge discovery process models and methodologies,” Knowl. Eng. Rev., vol. 25, no. 2, pp. 137–166, 2010, doi: DOI: 10.1017/S0269888910000032.
[16] S. Shahidi, A. Wahid Samadzai, and H. Shahbazi, “Effective Data Preprocessing in Data Science: From Method Selection to Domain-Specific Optimization,” Journal of Advanced Computer Knowledge and Algorithms, vol. 2, no. 4, pp. 84–90, Jul. 2025, doi: 10.29103/jacka.v2i4.22886.
[17] A. Hammad and S. AbouRizk, “Knowledge Discovery in Data: A Case Study,” Journal of Computer and Communications, vol. 02, no. 05, pp. 1–28, 2014, doi: 10.4236/jcc.2014.25001.
