普劳拉雅图村PKH援助聚类接受的k-均值方法分析

Dwi Kurnia Utami, Novica Irawati, Sumantri Sumantri
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引用次数: 0

摘要

家庭希望计划(PKH)是一项向极贫困户(RSTM)提供现金援助的计划,这些家庭需要满足与提高人力资源质量有关的要求。在选择居民作为拉雅岛村家庭希望计划(PKH)的接受者时,经常出现的问题是,家庭希望计划提供的援助往往被认为没有达到目标。此外,由于选择仍然是手工进行的,并且选择参与者需要很长时间,这可能受到PKH同伴客观评估的影响,因此经常出现错误。研究的目的是应用k-均值聚类算法来选择家庭希望计划(PKH)的潜在受益人。该方法使用了数据挖掘与k均值聚类算法的应用。基于k-means聚类算法的应用结果,正在构建的系统的结果可以更容易地选择潜在的家庭计划援助对象。k-means聚类算法测试的结果产生合格类别中的第1类,总计29个PKH受益人数据,不合格类别中的第2类,总计1个PKH受益人数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the k-Means Method in Clustering Acceptance of PKH Aid in Pulau Rakyat Tua Village
The Family Hope Program (PKH) is a program that provides cash assistance to Very Poor Households (RSTM) which are required to fulfill requirements related to efforts to improve the quality of human resources. In selecting residents to be recipients of the Family Hope Program (PKH) in Pulau Rakyat Tua Village, the problem that often arises is that the provision of Family Hope Program assistance is often considered not to be on target. In addition, errors often occur because the selection is still done manually and requires a long time in selecting participants, which can be influenced by the objective assessment of PKH companions. The research objective is to apply the k-means clustering algorithm in selecting prospective beneficiaries of the Family Hope Program (PKH). The method used uses the application of data mining with the k-means clustering algorithm. Based on the results of applying the k-means clustering algorithm, the results of the system being built can make it easier to select potential recipients of Family Program assistance. The results of the k-means clustering algorithm test produced Cluster 1 in the Eligible category totaling 29 PKH beneficiary data and Cluster 2 in the Ineligible category totaling 1 PKH beneficiary data.
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