Penerapan Metode K-Means dalam Pengelompokan Data Penduduk Tidak Mampu di Distrik Oransbari

Abstract

Proverty is a serious problem, the occurrence of proverty in the community is caused by a condition of the inability of the family economically to meet primary needs. Poor people are found in almost every country, city, and region. One of them is in the Oransbari District, which is one of the areas in Indonesia where the population does not receive assistance evenly. Based on these conditions, clustering is carried out to assist the district in grouping the poor population, so that the assistance provided can be right on target. With this problem, data mining with k-means clustering method used in clustering the poor population to make it easier for the Oransbari District to provide the population so that it is right on target. The data used is the data of the poor population in 2020 which amounted to 1872 with 17 attributes. Based on the results of tests carried out by applying the k-means algorithm, the results obtained with 3 clusters, the first cluster with a population of 471 with the category of poor population with medium priority, the second cluster has a population of 428 with a high priority category of poor population, and the third cluster has a population of 826 a low priority category of poor population. The k-means method is expected to be able to assist the Oransbari District in making decisions so that assistance is more targeted.