Data Mining: Building The Automatic Pipeline System for Clustering the Compliance Level of PBB-P2 Taxpayers

Sugeng Pranoto; Sri Wahyuni

Authors

Sugeng Pranoto Universitas Pembangunan Panca Budi
Sri Wahyuni Universitas Pembangunan Panca Budi

Keywords:

Data Mining, Pipeline, K-Means, Clustering, PBB-P2

Abstract

Taxpayer compliance analysis is crucial in regional revenue management, especially for Rural and Urban Land and Building Tax (PBB-P2). The large volume of taxpayer data poses a significant challenge in manual processing, so an automated and efficient approach is needed. This research develops a data mining pipeline to cluster the level of taxpayer compliance in the Badan Pengelola Keuangan dan Pendapatan Daerah (BPKPD) of Tebing Tinggi City. The proposed pipeline is implemented using the Java programming language and the SMILE library and includes three main procedures, namely Extraction, which is in charge of retrieving receivables and payment data from the SISMIOP database; Transformation, which is in charge of processing the extracted data to generate new insights through K-Means clustering, and Load is in charge of storing the transformation results into the MySQL database for further analysis and reporting. This pipeline is run every hour to ensure that data processing is carried out in real-time. By utilizing this automated system, this study aims to increase understanding of taxpayer compliance patterns and assist local governments in designing more effective policies to increase tax revenues and taxpayer compliance levels.

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