Taxpayer Classification Using K-Means Clustering to Support CRM Strategy Development: Case Study of Prabumulih City Samsat

  • Bimmo Fathin Tammam Sriwijaya University, Indonesia
  • Ali Ibrahim Sriwijaya University, Indonesia
  • Dwi Rosa Indah Sriwijaya University, Indonesia
  • Ahmad Fali Oklilas Sriwijaya University, Indonesia
  • Yadi Utama Sriwijaya University, Indonesia
DOI: 10.63158/journalisi.v7i4.1365
Keywords: K-Means Clustering, Taxpayer Segmentation, Customer Relationship Management, Davies–Bouldin Index, Public Service Analytics

Abstract

Effective management of taxpayer data is crucial for enhancing compliance and optimizing regional revenue. This study addresses the limited use of data-driven taxpayer segmentation in local Samsat institutions by applying K-Means Clustering to support targeted Customer Relationship Management (CRM) strategies. A dataset of 3,999 motor vehicle taxpayer records from September 2025 was processed through feature selection, scaling, and clustering. The analysis identified three distinct taxpayer groups based on payment timeliness, compliance consistency, and vehicle age. Cluster validity was confirmed using the Davies-Bouldin Index, yielding a value of -41.327 for k = 3, supported by ANOVA for statistical significance. The findings highlight how clustering can reveal taxpayer behavior patterns, guiding personalized services and compliance programs. This study's novelty lies in integrating clustering outcomes with practical CRM strategies for public agencies, offering a data-driven approach to improve taxpayer engagement and regional revenue. However, the study is limited by its focus on a single-period dataset and vehicle-related attributes.

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Published
2025-12-10
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How to Cite
Tammam, B., Ibrahim, A., Indah, D., Oklilas, A., & Utama, Y. (2025). Taxpayer Classification Using K-Means Clustering to Support CRM Strategy Development: Case Study of Prabumulih City Samsat. Journal of Information Systems and Informatics, 7(4), 3454-3482. https://doi.org/10.63158/journalisi.v7i4.1365
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Vol 7 No 4 (2025): December
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