Analysis of Covid-19 Cash Direct Aid (BLT) Acceptance Using K-Nearest Neighbor Algorithm

A. A. Aldino, Ryan Randy Suryono, Riyama Ambarwati
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引用次数: 1

Abstract

During the COVID-19 pandemic, the government imposed Large-Scale Social Restrictions (PSBB) to reduce or slow down the spread of COVID-19. This causes people to be unable to work as usual, and not even a few people have lost their jobs. This prompted the government to launch the Covid-19 direct cash assistance (BLT) program. One of the areas affected by the PSBB is Batu Ampar Village, which distributing BLT is considered less effective by residents because there are BLTs that are not well-targeted. The cause of the ineffectiveness of the distribution of aid was assessed because the data was out of sync; it was difficult to verify and validate the new data due to the size of the area and the constantly changing number of underprivileged residents. To overcome these problems, a model is needed to predict the recipients of this Covid-19 BLT. This study uses the K-Nearest Neighbor (K-NN) algorithm and RapidMiner tools to make predictions and validate using Cross-Validation. The data used are 711 lines with 474 training data and 237 testing data resulting in an accuracy of 89.68% for training data and 88.61% for testing data.
基于k近邻算法的Covid-19现金直接援助(BLT)接受度分析
在新冠肺炎大流行期间,政府实施了大规模社会限制(PSBB),以减少或减缓新冠肺炎的传播。这导致人们无法像往常一样工作,甚至没有少数人失业。这促使政府启动了新冠肺炎直接现金援助(BLT)计划。受PSBB影响的地区之一是Batu Ampar村,居民认为该村分发BLT的效果较差,因为有些BLT没有很好的针对性。评估了援助分配无效的原因,因为数据不同步;由于该地区的面积和贫困居民的数量不断变化,很难验证和验证新数据。为了克服这些问题,需要一个模型来预测这种新冠肺炎BLT的接受者。本研究使用K-最近邻(K-NN)算法和RapidMiner工具进行预测,并使用交叉验证进行验证。使用的数据是711行,474个训练数据和237个测试数据,导致训练数据的准确率为89.68%,测试数据的准确度为88.61%。
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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