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Open access Jurnal SINTA 2

Multi-Domain Sentiment Analysis on Ibu Kota Nusantara (IKN) Tweets Using CNN-LSTM

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 2 (2025) · pp. 153–164

DOI
10.22146/ijccs.104925
ISSN
2460-7258, 1978-1520
Published
2025-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The construction of Ibu Kota Nusantara (IKN) is a national project aimed at relocating Indonesia’s capital from Jakarta to East Kalimantan. This project has sparked various public opinions, which are widely expressed through social media platforms such as Twitter. Sentiment analysis of these opinions is crucial for understanding public perception of the IKN project. However, previous sentiment analysis studies have often overlooked domain variations in the analyzed data, such as economy, environment, and politics, each of which has distinct linguistic characteristics. This study aims to develop a multi-domain sentiment analysis model by comparing three main methods: CNN-LSTM, CNN, and LSTM. The multi-domain model is designed to address the differences in characteristics across domains and enhance the model’s ability to capture more complex sentiment patterns. The results indicate that multi-domain models outperform single-domain models, as they improve classification performance by leveraging information from multiple domains. CNN-LSTM proved to be the best model, achieving the most balanced Accuracy and F1-Score across various scenarios. The use of Keyword Embedding also significantly enhances model performance, particularly benefiting LSTM, which initially had the lowest performance.

Keywords

Collaboration network of the authors

28 authors · 28 co-authorship links · 7 across different organisations

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

  • Universitas Gadjah Mada
  • Indonesia University of Education
  • Universitas Airlangga
  • Instituto Profissional de Canossa - IPDC
  • Universitas Halu Oleo
  • Institut Teknologi Telkom Purwokerto
  • Universitas Widya Dharma Klaten
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sandi Fajar RodiyansyahIndonesia University of Education2
Mardi Siswo UtomoUniversitas Gadjah Mada1
Badrus ZamanUniversitas Airlangga1
Agustinus JacobusUniversitas Gadjah Mada1
Sulistyowati SulistyowatiAffiliation unknown1
Noviah Dwi PutrantiUniversitas Gadjah Mada1
Ambarwati AmbarwatiInstituto Profissional de Canossa - IPDC1
Agus Sasmito AriwibowoAffiliation unknown1
Eka KaryawatiUniversitas Gadjah Mada1
Robynson AmsekeAffiliation unknown1
Natalis RansiUniversitas Halu Oleo1
Sumarni AdiUniversitas Gadjah Mada1
Lutfi Budi IlmawanUniversitas Gadjah Mada1
Wayan Gede Suka ParwitaUniversitas Gadjah Mada1
Fadhila Tangguh AdmojoUniversitas Gadjah Mada1
Siti KhomsahInstitut Teknologi Telkom Purwokerto1
Doni SetyawanUniversitas Widya Dharma Klaten1
Fajar RatnawatiPoliteknik Negeri Bengkalis1
Ira ZulfaUniversitas Gadjah Mada1
Rakhmad IkhsanudinUniversitas Gadjah Mada1
Junanda PatihullahUniversitas Gadjah Mada1
Wafda RifaiUniversitas Gadjah Mada1
Sigit PriyantaUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Putri Rizki AmaliaUniversitas Gadjah Mada1
Angel Berta Desi SuryantiUniversitas Gadjah Mada1

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">Multi-Domain Sentiment Analysis on Ibu Kota Nusantara (IKN) Tweets Using CNN-LSTM</dc:title>
	<dc:creator>Prasastio, Fahmi Reza</dc:creator>
	<dc:creator>Winarko, Edi</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Multi-Domain Sentiment Analysis; CNN-LSTM; Word Embedding; Keyword Embedding</dc:subject>
	<dc:description xml:lang="en-US">The construction of Ibu Kota Nusantara (IKN) is a national project aimed at relocating Indonesia’s capital from Jakarta to East Kalimantan. This project has sparked various public opinions, which are widely expressed through social media platforms such as Twitter. Sentiment analysis of these opinions is crucial for understanding public perception of the IKN project. However, previous sentiment analysis studies have often overlooked domain variations in the analyzed data, such as economy, environment, and politics, each of which has distinct linguistic characteristics. This study aims to develop a multi-domain sentiment analysis model by comparing three main methods: CNN-LSTM, CNN, and LSTM. The multi-domain model is designed to address the differences in characteristics across domains and enhance the model’s ability to capture more complex sentiment patterns. The results indicate that multi-domain models outperform single-domain models, as they improve classification performance by leveraging information from multiple domains. CNN-LSTM proved to be the best model, achieving the most balanced Accuracy and F1-Score across various scenarios. The use of Keyword Embedding also significantly enhances model performance, particularly benefiting LSTM, which initially had the lowest performance.</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>2025-04-30</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/104925</dc:identifier>
	<dc:identifier>10.22146/ijccs.104925</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 2 (2025): April; 153-164</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/104925/41529</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 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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