Penerapan Data Mining untuk Mengcluster Data Penduduk Miskin Menggunakan Algoritma K-Means di Dusun Bagik Endep Sukamulia Timur

Abstract

The occurrence of poverty in the community is caused by a condition of the economic inability of the head of the family to meet the primary / basic needs of his family, namely the need for clothing, food, shelter and education. The poor community itself can be found in almost every country, city and region, for example in one of the Bagik Endep hamlets of East Sukamulia Village. Based on these conditions, it is necessary to carry out clustering to assist the village government in grouping poor families, so that assistance can be distributed appropriately. By observing the above problems, Data Mining is needed to classify aid recipients using the K-Means method in clustering the poor. Where the K-Means Clustering Algorithm method aims to classify population data in the East Sukamulia region who are said to be classified as poor. The data used is data on the population of East Sukamulia in 2019, amounting to 200 data with 9 attributes, namely the name of the population, occupation, income / month, the number of children attending elementary school, the number of children attending junior high school, the number of children attending high school, the number of children attending college , the number of children who are not in school and the number of family members. Based on the results of tests carried out by applying the K-Means algorithm, the results obtained are Cluster 1 totaling 18 residents with the criteria of high economic population, Cluster 2 totaling 72 residents with moderate economic population criteria, and Cluster 3 totaling 110 residents with low economic population criteria. The K-Means method is expected to be able to assist the government of Sukamulia Timur Village in making decisions and finding the information needed to solve problems in recording the poor population accurately