Penerapan Algoritma K-Means Untuk Mengelompokkan Kepadatan Penduduk Di Provinsi DKI Jakarta

Abstract

DKI Jakarta Province is an attraction for immigrants. If the population increases, if it cannot be resolved and managed well, it will result in bad things such as increasing the number of unemployed and affecting economic growth. Population data is used to help group regions based on population density in DKI Jakarta Province in 2019-2022 using the K-Means clustering method. From the results of the research, it provides a solution for the government to pay attention to population groups with the aim of preventing population density because it causes bad effects, so that community welfare is more guaranteed, so grouping (clustering) of provinces in DKI Jakarta is needed to provide information for people who wish to live in the Province DKI Jakarta. The research proves that the test results carried out clustering iterations of population density data were obtained in three iterations. For the results obtained by calculations using the K-Means method and using the rapidminer application, the results obtained were of the same value, namely the cluster with the highest population density of three districts/cities, namely South Jakarta, East Jakarta and West Jakarta whose population density continues to increase.

Keywords

• population density, data mining, clustering, k-means