Penerapan Data Mining Menggunakan Teknik Classification Untuk Melihat Potensi Kepatuhan Wajib Pajak Badan - Anuqman Fitriadi; Popalia, Qamarullah; Wibowo, Arief (Universitas Budi Darma, 2026)
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