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Open access Jurnal SINTA 2

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

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 2 (2022) · pp. 193–204

DOI
10.22146/ijccs.70801
ISSN
2460-7258, 1978-1520
Published
2022-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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

Collaboration network of the authors

3 authors · 3 co-authorship links · 2 across different organisations

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  • Universitas Teknokrat Indonesia
  • Universitas Islam Negeri Raden Intan Lampung
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Analysis of Covid-19 Cash Direct Aid (BLT) Acceptance Using K-Nearest Neighbor Algorithm</dc:title>
	<dc:creator>Aldino, Ahmad Ari</dc:creator>
	<dc:creator>Suryono, Ryan Randy</dc:creator>
	<dc:creator>Ambarwati, Riyama</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">COVID-19; Data Mining; K-NN; Cross-Validation</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/70801</dc:identifier>
	<dc:identifier>10.22146/ijccs.70801</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 2 (2022): April; 193-204</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/70801/33700</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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