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Jurnal SINTA 2

TWEET CLASSIFICATION USING DEEP LEARNING ARCHITECTURE FOR CONCERT EVENT DETECTION

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 13 No. 2 (2020) · pp. 57–63

DOI
10.21609/jiki.v13i2.815
ISSN
2502-9274, 2088-7051
Published
2020-07-01
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Twitter social media is used by millions of users to share stories about their lives. There are millions of tweets sent by Twitter users in a short amount of time. These tweets can contain information about an incident, complaints from Twitter users, and others. Finding information about events from existing tweets requires great effort. Therefore, this study proposed a system that can detect events based on tweets using the CNN-LSTM architecture. Based on the classification testing obtained precision results of 70.97%, and recall amounted to 63.76%. The results obtained are good enough as a first step to detect events on Twitter.

Collaboration network of the authors

40 authors · 87 co-authorship links · 10 across different organisations · showing the 40 most connected

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

  • Institut Teknologi Sepuluh Nopember
  • Lampung University
  • University of Brawijaya
  • Universitas Islam Negeri Raden Intan Lampung
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Diana PurwitasariInstitut Teknologi Sepuluh Nopember6
Dini Adni NavastaraInstitut Teknologi Sepuluh Nopember4
Rarasmaya IndraswariInstitut Teknologi Sepuluh Nopember3
Munjiah Nur SaadahInstitut Teknologi Sepuluh Nopember2
Rigga Widar AtmagiInstitut Teknologi Sepuluh Nopember2
Dyah S. RahayuInstitut Teknologi Sepuluh Nopember2
Khoirul UmamInstitut Teknologi Sepuluh Nopember2
Gulpi Qorik Oktagalu PratamasunuAffiliation unknown2
Anny YuniartiInstitut Teknologi Sepuluh Nopember2
Darlis HerumurtiInstitut Teknologi Sepuluh Nopember2
Safri AdamInstitut Teknologi Sepuluh Nopember2
Wawan GunawanUniversitas Islam Negeri Raden Intan Lampung1
⁠Undang RosidinLampung University1
Nina KadaritnaLampung University1
Naser JawasInstitut Teknologi Sepuluh Nopember1
Randy Cahya WihandikaUniversity of Brawijaya1
Go Frendi GunawanInstitut Teknologi Sepuluh Nopember1
Sonny Christiano GosariaInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Evy Kamilah RatnasariInstitut Teknologi Sepuluh Nopember1
Siti MutrofinInstitut Teknologi Sepuluh Nopember1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mohammad ZarkasiInstitut Teknologi Sepuluh Nopember1
Fidi Wincoko PutroInstitut Teknologi Sepuluh Nopember1
Khadijah Fahmi Hayati HolleInstitut Teknologi Sepuluh Nopember1
Andi Baso KaswarInstitut Teknologi Sepuluh Nopember1
Saprina MamaseInstitut Teknologi Sepuluh Nopember1
Saiful Bahri MusaInstitut Teknologi Sepuluh Nopember1
Ahmad Mustofa HadiInstitut Teknologi Sepuluh Nopember1
Christian Sri Kusuma AdityaInstitut Teknologi Sepuluh Nopember1
Mamluatul Hani`ahInstitut Teknologi Sepuluh Nopember1
Aryo HartoInstitut Teknologi Sepuluh Nopember1
Arif FadllullahInstitut Teknologi Sepuluh Nopember1
Dasrit Debora KamudiInstitut Teknologi Sepuluh Nopember1
Muhamad NasirInstitut Teknologi Sepuluh Nopember1

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://jiki.cs.ui.ac.id/index.php/jiki/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">TWEET CLASSIFICATION USING DEEP LEARNING ARCHITECTURE FOR CONCERT EVENT DETECTION</dc:title>
	<dc:creator>Purnomo, Adenuar</dc:creator>
	<dc:creator>Naufal, Ahmad Afiif</dc:creator>
	<dc:creator>Yudha, Ery Permana</dc:creator>
	<dc:creator>Arifin, Agus Zainal</dc:creator>
	<dc:description xml:lang="en-US">Twitter social media is used by millions of users to share stories about their lives. There are millions of tweets sent by Twitter users in a short amount of time. These tweets can contain information about an incident, complaints from Twitter users, and others. Finding information about events from existing tweets requires great effort. Therefore, this study proposed a system that can detect events based on tweets using the CNN-LSTM architecture. Based on the classification testing obtained precision results of 70.97%, and recall amounted to 63.76%. The results obtained are good enough as a first step to detect events on Twitter.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2020-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/815</dc:identifier>
	<dc:identifier>10.21609/jiki.v13i2.815</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 13 No. 2 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 57-63</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v13i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/815/429</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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