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

https://doi.org/10.22146/ijccs.70801

Ahmad Ari Aldino(1*), Ryan Randy Suryono(2), Riyama Ambarwati(3)

(1) Department of Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia
(2) Department of Information System, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia
(3) Department of Mathematics Education, Faculty of Tarbiyah and Teacher Training, UIN Raden Intan Lampung
(*) Corresponding Author

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.

Keywords


COVID-19; Data Mining; K-NN; Cross-Validation

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DOI: https://doi.org/10.22146/ijccs.70801

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