Classification of Eligibility for BPNT Recipients Using the K-Nearest Neighbor Algorithm

Abstract

BPNT (Non-Cash Food Assistance) is food social assistance in non-cash form from the government which is given to Beneficiary Families (KPM) every month. In its implementation, BPNT is still encountering a number of obstacles, one of which is in terms of distribution of aid which has not been optimal in several areas, including in Mekar Sari Village, Kec.Pulau Rakyat, Kab. Sharpen. In carrying out the BPNT program, many residents complained that they did not receive this assistance, but they felt they had the right to receive assistance like the others. The aim of the research is to apply the K-Nearest Neighbor algorithm so that it can help the process of classifying data on citizens who are eligible or not eligible to receive non-cash food assistance (BPNT). The method used uses the application of data mining classification techniques with the K-Nearest Neighbor algorithm. Based on the results of implementing the K-Nearest Neighbor data mining algorithm, the results of the system created can predict and help the village government to make decisions and describe residents who are eligible and not eligible for BPNT assistance using data on poor residents recorded in Mekar Sari Village, Kec. People's Island, Kab. Asahan by using the K-Nearest Neighbor algorithm data mining system.