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Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 2 (2020) · pp. 179–188

DOI
10.22146/ijccs.54507
ISSN
2460-7258, 1978-1520
Published
2020-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Children are the future of the nation. All treatment and learning they get would affect their future. Nowadays, there are various kinds of social problems related to children. To ensure the right solution to their problem, social workers usually refer to the social-child-case (SCC) documents to find similar cases in the past and adapting the solution of the cases. Nevertheless, to read a bunch of documents to find similar cases is a tedious task and needs much time. Hence, this work aims to categorize those documents into several groups according to the case type. We use topic modeling with Latent Dirichlet Allocation (LDA) approach to extract topics from the documents and classify them based on their similarities. The Coherence Score and Perplexity graph are used in determining the best model. The result obtains a model with 5 topics that match the targeted case types. The result supports the process of reusing knowledge about SCC handling that ease the finding of documents with similar cases

Keywords

Collaboration network of the authors

25 authors · 51 co-authorship links · 5 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Teknologi Yogyakarta
  • Universitas Amikom Yogyakarta
  • Hasanuddin University
  • Sriwijaya University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Noor Akhmad SetiawanAffiliation unknown3
Oyas WahyunggoroUniversitas Gadjah Mada2
Adha Imam CahyadiUniversitas Gadjah Mada2
Hendri Himawan TriharmintoUniversitas Gadjah Mada2
Igi ArdiyantoUniversitas Gadjah Mada2
Anang Anggono LutfiUniversitas Gadjah Mada2
Silmi FauziatiUniversitas Gadjah Mada2
Indriana HidayahUniversitas Gadjah Mada2
Saucha DiwandariUniversitas Teknologi Yogyakarta1
Pulung Hendro PrastyoUniversitas Gadjah Mada1
Amin Siddiq SumiUniversitas Gadjah Mada1
Ade Widyatama DianUniversitas Gadjah Mada1
Selo SulistyoUniversitas Gadjah Mada1
Cakra Aminuddin HamkaAffiliation unknown1
Sri Suning KusumawardaniUniversitas Gadjah Mada1
Brillian StefaniAffiliation unknown1
I Made Artha AgastyaUniversitas Amikom Yogyakarta1
Adi WijayaHasanuddin University1
Rifkie PrimarthaSriwijaya University1
Hanung Adi NugrohoUniversitas Gadjah Mada1
Fauziazzuhry RahadianAffiliation unknown1
Ratna Lestari Budiani BuanaAffiliation 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 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation</dc:title>
	<dc:creator>Tresnasari, Nur Annisa</dc:creator>
	<dc:creator>Adji, Teguh Bharata</dc:creator>
	<dc:creator>Permanasari, Adhistya Erna</dc:creator>
	<dc:subject xml:lang="en-US">Information Technology; Text Mining</dc:subject>
	<dc:subject xml:lang="en-US">Text Clustering; Topic Modeling; Latent Dirichlet Allocation; Social Child Case</dc:subject>
	<dc:description xml:lang="en-US">Children are the future of the nation. All treatment and learning they get would affect their future. Nowadays, there are various kinds of social problems related to children.  To ensure the right solution to their problem, social workers usually refer to the social-child-case (SCC) documents to find similar cases in the past and adapting the solution of the cases. Nevertheless, to read a bunch of documents to find similar cases is a tedious task and needs much time. Hence, this work aims to categorize those documents into several groups according to the case type. We use topic modeling with Latent Dirichlet Allocation (LDA) approach to extract topics from the documents and classify them based on their similarities. The Coherence Score and Perplexity graph are used in determining the best model. The result obtains a model with 5 topics that match the targeted case types. The result supports the process of reusing knowledge about SCC handling that ease the finding of documents with similar cases</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>2020-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/54507</dc:identifier>
	<dc:identifier>10.22146/ijccs.54507</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 2 (2020): April; 179-188</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/54507/27888</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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>