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Open access Jurnal SINTA 2

Covid-19 Hoax Detection Using KNN in Jaccard Space

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 3 (2021) · pp. 255–264

DOI
10.22146/ijccs.67392
ISSN
2460-7258, 1978-1520
Published
2021-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief & Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief & Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.

Keywords

Collaboration network of the authors

30 authors · 60 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Amikom Yogyakarta
  • State University of Malang
  • Universitas AKPRIND Indonesia
  • Institut Teknologi Sepuluh Nopember
  • Universitas Amikom Purwokerto
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Wahyu Krisna HidayatUniversitas Amikom Yogyakarta3
Agung Prasetio BudiUniversitas Amikom Yogyakarta3
Agung Budi PrasetioUniversitas Amikom Yogyakarta2
Andi SunyotoUniversitas Amikom Yogyakarta2
Mursyid ArdiansyahUniversitas Amikom Yogyakarta2
Suwanto RaharjoUniversitas AKPRIND Indonesia2
Erwin SyahrudinUniversitas Amikom Yogyakarta2
Catur Riyono Heri WibowoUniversitas Amikom Yogyakarta1
HanafiAffiliation unknown1
Kartikasari Kusuma AgustiningsihUniversitas Amikom Yogyakarta1
Muhammad Altoumi AlsyaibaniUniversitas Amikom Yogyakarta1
Kartika Candra KiranaInstitut Teknologi Sepuluh Nopember1
Slamet WibawantoState University of Malang1
Ahmad HamdanState University of Malang1
Wiga Maulana BaihaqiUniversitas Amikom Purwokerto1
Iqbaluddin Syam HadAffiliation unknown1
Veny Cahya HarditaUniversitas Amikom Yogyakarta1
Emha Taufiq LuthfiUniversitas Amikom Yogyakarta1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Arief SetyantoUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1
Mei KurniawanAffiliation unknown1
M. SuyantoAffiliation unknown1
KusriniAffiliation unknown1
Aiman MukhlishahAffiliation unknown1

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">Covid-19 Hoax Detection Using KNN in Jaccard Space</dc:title>
	<dc:creator>Utami, Ema</dc:creator>
	<dc:creator>Iskandar, Ahmad Fikri</dc:creator>
	<dc:creator>Hidayat, Wahyu</dc:creator>
	<dc:creator>Prasetyo, Agung Budi</dc:creator>
	<dc:creator>Hartanto, Anggit Dwi</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science, Artificial Intelligent, Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Hoax; Covid-19; KNN; Jaccard; Nazief &amp; Adriani</dc:subject>
	<dc:description xml:lang="en-US">Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief &amp; Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief &amp; Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.</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>2021-07-31</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/67392</dc:identifier>
	<dc:identifier>10.22146/ijccs.67392</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 3 (2021): July; 255-264</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/67392/31839</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 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>
</oai_dc:dc>