Penerapan Metode K-Means Clustering Untuk Mengelompokkan Data Kelayakan Penerima Bantuan Renovasi Rumah

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-27 DOI:10.47065/bits.v5i2.4298

Gina Sonia, Raissa Amanda Putri

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Abstract

The government provided one form of assistance, namely the renovation of houses in the Kuala Bangka Village area in Kualuh Hilir District, North Labuhan Batu Regency from 2015 until now. However, with this assistance, Kuala Bangka Village sometimes has problems in determining the feasibility of receiving assistance from the government, therefore the author will conduct research by categorizing the eligibility of recipients of house renovation assistance by applying the K-Means algorithm. The K-Means Clustering algorithm is an algorithm that can classify data accurately according to the previous problem. The grouping aims to determine cluster 0 and cluster 1 as recipients of house renovation assistance, cluster 0 is feasible and cluster 1 is not feasible. The attributes used in this research are number of family members, employment, housing conditions, and income. The results obtained from 170 data were cluster 0 with 91 data and cluster 1 with 79 data. From these results, 91 people were eligible to receive home renovation assistance and 79 were not eligible to receive home renovation assistance

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应用k - mememememememememememememememedata来分类

从2015年至今，政府提供了一种形式的援助，即在北拉布汗巴图县Kualuh Hilir区的Kuala Bangka村地区翻新房屋。然而，在这种帮助下，Kuala Bangka Village有时在确定接受政府援助的可行性方面存在问题，因此作者将通过应用K-Means算法对房屋翻新援助接受者的资格进行分类来进行研究。K-Means聚类算法是一种能够根据前面的问题对数据进行准确分类的算法。分组的目的是确定集群0和集群1作为房屋改造援助的接受者，集群0是可行的，集群1是不可行的。在这项研究中使用的属性是家庭成员的数量，就业，住房条件和收入。170条数据得到的结果为第0类91条数据，第1类79条数据。根据这些结果，有91人符合资格获得家居装修援助，而79人不符合资格获得家居装修援助

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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Building of Informatics, Technology and Science (BITS)

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