<![CDATA[Abstract: The biggest challenge in managing the Rural and Urban Land and Building Tax (PBB-P2) lies in the large amount of taxpayer data that must be managed and analyzed as the basis for making accurate policies and decisions. Data mining and Business Intelligence (BI) provide a strong foundation for organizations to manage and analyze data effectively in support of decision-making. The integration of Data Mining in clustering taxpayer compliance levels using the K-Means algorithm and Business Intelligence (BI) through Apache Superset for monitoring the realization of PBB-P2 receivables can be implemented using the Research & Development (R&D) method. The stages of this method include problem identification, literature study, data collection, system integration design and implementation, system integration evaluation, analysis of integration results, and the final stage consisting of conclusions and recommendations from the research. The research results show that the K-Means Clustering algorithm in Data Mining is effective in clustering taxpayers based on their compliance level in paying the PBB-P2. Furthermore, the integration of Data Mining applications based on Java and Business Intelligence (BI) using Apache Superset can optimize the monitoring of PBB-P2 receivables realization in BPKPD Tebing Tinggi. Keywords: Data Mining, Business Intelligence, K-Means, Java, Apache Superset, PBB-P2 Abstrak: Tantangan terbesar dalam pengelolaan Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) terletak pada besarnya jumlah data wajib pajak yang dikelola dan akan dianalisa sebagai dasar pengambilan kebijakan dan keputusan yang tepat. Data maining dan Business Intelligence (BI) memberikan landasan yang kuat bagi organisasi dalam mengelola dan menganalisis data secara efektif untuk mendukung pengambilan keputusan. Penerapan integrasi Data Mining dalam pengelompokan tingkat kepatuhan wajib pajak dengan menggunakan algoritma K-Means dan Business Intelligence (BI) dengan menggunakan apache superset pada monitoring realisasi piutang Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) dapat dilakukan dengan menggunakan metode Research & Development (R&D). Tahapan metode tersebut meliputi identifikasi masalah, studi literatur, pengumpulan data, peracangan dan implementasi sistem integrasi, evaluasi sistem integrasi, analisa hasil integrasi dan tahapan akhir adalah kesimpulan dan rekomendasi dari penelitian. Hasil dari penelitian menunjukan algoritma K-Means Clustering dalam Data Mining efektif untuk mengelompokkan wajib pajak berdasarkan tingkat kepatuhan pembayaran Pajak PBB-P2 dan dengan integrasi aplikasi Data Mining berbasis Java dan Business Intelligence (BI) menggunakan Apache Superset dapat mengoptimalkan monitoring realisasi piutang PBB-P2 di BPKPD Tebing Tinggi. Kata kunci: Promosi, Brosur, Multimedia Development Life Cycle, Augmented Reality]]>
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