Implementasi Data Mining Untuk Penerima Bantuan PKH Pemerintah dengan Menerapkan Algoritma Klastering K-Medoids

Yunan Fauzi Wijaya

doi:10.47065/josyc.v5i3.5197

Yunan Fauzi Wijaya * Universitas Nasional, Jakarta, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josyc.v5i3.5197

Keywords: Data Mining; Reception; Help; PKH; K-Medoids Clustering Algorithm

Abstract

In 2019, a very heartbreaking event occurred for the entire world population. Where the consequences of the outbreak caused total paralysis of all activities in the world. The impact of non-performance of economic activities has caused the paralysis of the economy throughout the world, not only affecting small companies but also large companies. Especially in Indonesia itself, during the Covid-19 pandemic, there were many large-scale employee layoffs. The impact of this is increasing the number of family poverty cases in Indonesia. The Family Hope Program (PKH) is a program run by the government through the Ministry of Social Affairs. Even though the PKH program is based on the implementation of the Ministry of Social Affairs, the determination process is carried out by each social service in each region. There are still many families who are poor families who actually do not receive PKH assistance. This problem is caused by the large number of families in an area which requires quite a long process. The determination of poor families determined by the relevant agencies should be able to be seen based on data on previous PKH aid recipients. Data mining is a data mining process, data mining is carried out with the aim of obtaining new information that is valuable and important. Clustering or Clustering is part of data mining which aims to group data. Clustering is the formation of a new cluster from previously existing data. The K-Medoids algorithm is an algorithm for clustering data mining. In the K-Medoids algorithm, a process is carried out based on calculating the closest distance. From the process that has been carried out, it is estimated that there are 2 (two) clusters formed where in cluster 1 there are 7 families included in it. Meanwhile, in cluster 2 there are 8 families included.

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