Analisa Pemetaan Penerimaan Bantuan Kesejahteraan Masyarakat Terdampak Covid-19 Dengan Algoritma Clustering

Authors

  • Erlin Elisa Universitas Putera Batam
  • Tukino Tukino Universitas Putera Batam
  • Muhammad Taufik Syastra Universitas Putera Batam

Keywords:

Covid-19, Welfare Assistance, Data Mining, Clustering, K-Means

Abstract

2020 is a very difficult year for humans in this world, with a virus outbreak that has swept across the country, this outbreak is called Coronavirus Disease 2019 (Covid-19). Including in Indonesia with cases that continue to increase every day, until now there is no sign of this outbreak ending, with this incident the government issued a policy for large-scale social restrictions or known as PSPB in various regions that fall into the category of large case zones and According to data from the Riau Islands Covid-19 cluster, in Batam City itself, the number of Covid-19 cases continued to increase so that it exceeded the number of 2000. As a result of this protracted disaster, of course, human life has been disrupted, many jobs have been neglected, so that various types of businesses have had to go out of business because their turnover has been much reduced and this has caused business voters to have to lay off their employees. This situation received rapid attention from the government through the social service agency to provide welfare assistance for people affected by COVID-19, but the problem here is from observations while researchers are still many of the assistance that is not right on target, causing polemics for the community. This research will utilize one of the datamining techniques with the K means algorithm to determine the mapping or segmentation of government assistance to communities affected by COVID-19 in order to accelerate people's purchasing power with the aim that research results can be used by community leaders such as RT, RW to record citizens who need this assistance. The results of the analysis show that the clustering segmentation with the data analyzed is 30 data stating that there are priority recipients which are divided into 5 clusters with predetermined criteria.

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Published

2022-01-22

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