Implementasi Algoritma K-Means Clustering Dalam Mengelompokkan Kepatuhan Wajib Pajak Bumi dan Bangunan Dengan Optimasi Elbow - Nur, Amri Muliawan; Hariman Bahtiar; Mila Agustiarini Jannah (Infotek: Jurnal Informatika dan Teknologi, 2025)
Land and Building Tax (Pajak Bumi dan Bangunan, or PBB) is one of the primary sources of regional revenue that plays a significant role in supporting development across various regions. Therefore, efforts to improve tax compliance must be enhanced through various strategies, such as continuous socialization and education, to raise awareness of the importance of paying taxes. Additionally, improving the quality of services is essential. This study aims to classify the compliance levels of PBB taxpayers in Sakra District using the K-Means Clustering algorithm. The data used in this research is the 2023 PBB dataset for Sakra District, comprising 376 entries and involving five key attributes: land area, building area, PBB assessment, payment status, and penalties. The results obtained from processing using the K-Means algorithm indicate an optimal number of clusters, as follows: Cluster 1 represents a high compliance level, consisting of 355 items; Cluster 2 represents a moderate compliance level, consisting of 18 items; and Cluster 3 represents a low compliance level, consisting of 3 items. These clustering outcomes can serve as a reference for authorities in formulating more targeted strategies to enhance tax compliance through improved education and services in the future.