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Umam, Muhammad Taufiqul
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Computer Science & IT Decision Sciences, Operations Research & Management

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Identifikasi Tingkat Kepatuhan Wajib Pajak Bumi dan Bangunan di Jakarta dengan Pendekatan Algoritma K-Means Umam, Muhammad Taufiqul; Basyah, Baby Lolita
Jurnal Ilmiah Informatika Komputer Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2025.v30i3.59

Abstract

The Rural and Urban Land and Building Tax (PBB-P2) serves as a regional fiscal mechanism levied on the ownership, control, or utilization of property assets by both individuals and corporate entities. As a cornerstone of regional fiscal policy, PBB-P2 is instrumental in bolstering Local Own-Source Revenue (PAD). Given Jakarta's status as the nation’s administrative and commercial epicenter characterized by high population density and intense economic momentum, the city holds a strategic and vast potential for PBB-P2 collection. This study aims to categorize the compliance behavior of PBB-P2 taxpayers within the Jakarta region by utilizing the K-Means algorithm. The research methodology is guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, which involves six systematic phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset consists of 28,125 PBB-P2 taxpayer records collected from 2020 to 2024. The findings reveal that taxpayer compliance is classified into four distinct clusters. Cluster 0 indicates very high compliance, Cluster 1 reflects low compliance, Cluster 2 denotes moderate compliance, and Cluster 3 corresponds to high compliance. An average silhouette coefficient value of 0.742 demonstrates that the resulting clusters are well-defined, showing strong internal similarity and clear separation from one another.
Co-Authors Baby Lolita B., Baby Lolita