Analysis of K-Means Algorithm for Clustering of Covid-19 Social Assistance Recipients

Sri Rahmayani, S. Sumarno, Zulia Almaida Siregar
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Abstract

During the Covid-19 pandemic, the government provided assistance distributed through each sub-district throughout the province of Indonesia, one of which was the Pahlawan Village in the East Siantar District Pematangsiantar City. So far, the assistance provided by Kelurahan Pahlawan is still done manually, so errors in data collection and distribution of aid may occur. To overcome this problem, a study was carried out by applying the K-Means algorithm to determine the eligibility cluster of Covid-19 beneficiaries, which was carried out by collecting population data according to predetermined attributes. Then the population data will be clustered using the K-Means algorithm and tested using the Rapid Miner application. The clustering results obtained are that cluster 0 consists of 26 data and that cluster 1 consists of 24 data. The recipients of Covid-19 social assistance using the K-Means algorithm show that those entitled to receive the gift are the elderly (elderly). Based on this, it can be concluded that the K-Means Algorithm can be applied to produce more practical information in determining who is entitled to receive assistance
新型冠状病毒社会救助受助人k -均值聚类分析
在2019冠状病毒病大流行期间,政府通过印度尼西亚全省的每个街道提供了援助,其中一个是东贤达区佩马唐贤达市的Pahlawan村。目前为止,Kelurahan Pahlawan提供的援助仍然是手工完成的,因此可能会在数据收集和分发援助方面出现错误。为了克服这一问题,采用K-Means算法,根据预定属性收集人口数据,确定Covid-19受益人的资格聚类,进行了一项研究。然后,将使用K-Means算法对总体数据进行聚类,并使用Rapid Miner应用程序进行测试。得到的聚类结果是,集群0由26个数据组成,集群1由24个数据组成。用K-Means算法计算的新冠疫情社会救助对象中,有资格领取礼物的是老年人(老人)。基于此,可以得出结论,K-Means算法可以在确定谁有资格获得援助方面产生更实用的信息
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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