Analisis Kelayakan Penerima Bantuan Covid-19 Menggunakan Metode K–Means

Abstract

The government provides several types of assistance during the covid-19 pandemic that is distributed through the agencies of each village throughout Indonesia, one of which is Punggulan Village air joman subdistrict. The types of assistance that have been distributed to citizens are Cash Social Assistance (BST), Social Safety Net Assistance (JPS), Non-Cash Sembako Assistance, and Cash Direct Assistance (BLT). So far the assistance provided by Punggulan Village is still done manually, so it is possible to occur errors in the collection and distribution of assistance. To solve the problem, the author applies one of the data mining algorithms, the K-Means algorithm, to determine the recipient of covid-19 assistance that is done by collecting population data by the specified attributes. Then the data is weighted to facilitate the calculation of K-Means, after that build a system to implement the K-Means algorithm and perform testing. Population data used is 50 data recipients of aid 2021 as a sample using 3 attributes, namely, income, dependents, and other beneficiaries. The result of this system is prospective recipients of Covid-19 assistance with 2 feasible clusters (C1) and unfit (C2)