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Identify Reviews of Pedulilindungi Applications using Topic Modeling with Latent Dirichlet Allocation Method

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 4 (2023) · pp. 441–450

DOI
10.22146/ijccs.86025
ISSN
2460-7258, 1978-1520
Published
2023-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The emergence of Covid-19 in December 2019 has disrupted life worldwide, including Indonesia. The government has made various efforts to control the pandemic, one of which is the development of an application called PeduliLindungi. This app aims to be a reliable tool for the government and the entire community during the pandemic. As a new regulation, the use of PeduliLindungi has prompted numerous reviews assessing its quality and performance. With the app's emergence and growth, various topics have emerged and become trending among the public. These topics were identified through user reviews of the PeduliLindungi app, using the Latent Dirichlet Allocation (LDA) algorithm. The data, consisting of 15,522 reviews, was collected from the Google Play Store and underwent pre-processing, including dictionary and corpus creation, determining the number of topics, and modeling with LDA. The resulting topic modeling process generated the ten most prominent topics. The outcomes were visualized using word clouds and topic distribution graphs, representing the most discussed aspects of the PeduliLindungi app among users. These topics are considered diverse since each issue has no relation or similarity to one another.

Keywords

Collaboration network of the authors

9 authors · 12 co-authorship links

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

  • Gunadarma University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Brizky Ramadhani IsmantoGunadarma University1
Tubagus Maulana KusumaGunadarma University1
Muhamad SidikGunadarma University1
Bambang GunawanGunadarma University1
Dede IskandarAffiliation unknown1
Dede IskandarGunadarma University1

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 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Identify Reviews of Pedulilindungi Applications using Topic Modeling with Latent Dirichlet Allocation Method</dc:title>
	<dc:creator>Hardiyanti, Layli</dc:creator>
	<dc:creator>Anggraini, Dina</dc:creator>
	<dc:creator>Kurniawati, Ana</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Information System</dc:subject>
	<dc:description xml:lang="en-US">The emergence of Covid-19 in December 2019 has disrupted life worldwide, including Indonesia. The government has made various efforts to control the pandemic, one of which is the development of an application called PeduliLindungi. This app aims to be a reliable tool for the government and the entire community during the pandemic. As a new regulation, the use of PeduliLindungi has prompted numerous reviews assessing its quality and performance. With the app's emergence and growth, various topics have emerged and become trending among the public. These topics were identified through user reviews of the PeduliLindungi app, using the Latent Dirichlet Allocation (LDA) algorithm. The data, consisting of 15,522 reviews, was collected from the Google Play Store and underwent pre-processing, including dictionary and corpus creation, determining the number of topics, and modeling with LDA. The resulting topic modeling process generated the ten most prominent topics. The outcomes were visualized using word clouds and topic distribution graphs, representing the most discussed aspects of the PeduliLindungi app among users. These topics are considered diverse since each issue has no relation or similarity to one another.</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>2023-10-31</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/86025</dc:identifier>
	<dc:identifier>10.22146/ijccs.86025</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 4 (2023): October; 441-450</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/86025/37715</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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