Penerapan Algoritma K-Means Clustering Untuk Pemetaan Kepadatan Penduduk Berdasarkan Jumlah Penduduk Kota Medan - Marpaung, Preddy; Siahaan, R. Fanry (J-SAKTI (Jurnal Sains Komputer dan Informatika), 2021)
Population density in a large city such as Medan will have many impacts on the community. However, often people, either individuals or groups who want to live or live in the city of Medan, choose their location at will without knowing the existing population density classification, so that they can have a big problem impact on the community or tend to be in a circle of huge problems they will face. if you do not know, choose the place of residence that the community will occupy. The community's ignorance of the location of population density in the city of Medan is due to the absence of knowledge or information on population density mapping. So it is necessary to map the population density as new knowledge for the community to avoid or reduce the impact that will be experienced by people who want to live or reside in the city of Medan. This population density mapping will be grouped into 3 groups (clusters) using the K-Means Cluster algorithm, namely very dense (cluster1), dense (cluster2), and medium (cluster3). The results of population density mapping in the city of Medan, namely the very densely populated area of 121 kelurahan, the densely populated area is 30 sub-districts, and areas are no longer found in the city of Medan.