K-Means Clustering in Determining the Eligibility of Recipients of Assistance for the Poor Case Study of Village Sukoharjo III

Abstract

Poor people are residents who have an average per capita income below the poverty line. Assistance funds for the poor are one of the government's program efforts to reduce the level of community poverty. The purpose of this research is to test the eligibility of beneficiaries so that they are right on target - really poor people who deserve to receive this assistance. This is because there are many criteria that must be considered in determining the eligibility of recipients of aid funds with a total of 4072 residents, the researchers used data utilization techniques or also called Data Mining. One of the data mining methods is quite popular, namely clustering using the K-Means algorithm. Processing the selection data for receiving aid funds using the K-Means algorithm results in a Davies bouldin index of 0.738. These results are considered quite good because the closer the results are to zero, this study produces three groups: 1218 residents are eligible to receive assistance, 2514 residents are considered to receive assistance, 1040 are not eligible to receive assistance.