SYSTEMATIC LITERATURE REVIEW: PEMETAAN KARAKTERISTIK DENGAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS - Rizqi Afif Izzuddin; Cakra Trinata; Okky Pratama Martadiredja (Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan, 2025)
K-Means is a non-hierarchical data clustering method based on data similarity, capable of grouping data into several clusters. In other words, data with similar characteristics are grouped into the same cluster, while data with differing characteristics are placed in separate clusters. The K-Means method can be applied to various types of data, including those in the governmental sector. Although the K-Means algorithm has been widely utilized, its application in government-related activities remains limited, often restricted to selection or recruitment processes. Moreover, the use of attributes in such studies needs to be expanded to achieve more optimal results. This study reviews several articles that implement the K-Means method in research related to public administration. Based on the findings, journals discussing the use of the K-Means algorithm for clustering in government contexts are proven to be relevant and beneficial for future research. It can be concluded that the K-Means method is a validated approach and can be effectively employed for clustering in the public sector. This method also offers advantages across various aspects of governance, benefiting stakeholders, the general public, and other administrative domains.