Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent Dirichlet Allocation

Matira, Yayang; Junaidi; Setiawan, Iman

doi:10.20956/ejsa.vi.24843

Journal

Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent Dirichlet Allocation

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 1 (2023) · pp. 53–63

DOI: 10.20956/ejsa.vi.24843
ISSN: 2721-379X, 2721-3803
Published: 2023-02-14
Publisher: Hasanuddin University
Language: en

Abstract

Detikcom is a very popular news portal today. The news on the portal continues to grow time to time, causing the existing news data to pile up. As a result, this is necessary to utilize this large amount of data. One of the ways that can be used is to extract topics from news text data through topic modeling using the Latent dirichlet allocation (LDA) method. This method is very popular because it can perform analysis on very large documents. This research aims to find certain patterns in a document by generating several different topics so that it does not specifically divide documents into a particular topic. This research has three topics obtained, with a coherence score is 0,7586. The first topic discusses conflicts and crises within a country, the second topic discusses issues related to humanitarian, and the third topic discusses the issues of corruption committed by state officials.

Keywords

Collaboration network of the authors

13 authors · 34 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.

Tadulako University
North Sumatra Islamic University
Institut Teknologi Sepuluh Nopember
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Fadjryani Fadjryani	Tadulako University	3
Junaidi Junaidi	North Sumatra Islamic University	1
Mohammad Batara	Tadulako University	1
Syaiful Hendra	Tadulako University	1
Nurmasita Ismail	Affiliation unknown	1
Virga Damayanti	Tadulako University	1
Junaidi Junaidi	Institut Teknologi Sepuluh Nopember	1
Dini Aprilia Afriza	Tadulako University	1
Rais Rais	Tadulako University	1
Hartayuni Sain	Tadulako University	1

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.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent  Dirichlet Allocation</dc:title>
	<dc:creator xml:lang="en">Matira, Yayang</dc:creator>
	<dc:creator xml:lang="en">Junaidi</dc:creator>
	<dc:creator xml:lang="en">Setiawan, Iman</dc:creator>
	<dc:subject xml:lang="en">Coherence Score</dc:subject>
	<dc:subject xml:lang="en">Detikcom</dc:subject>
	<dc:subject xml:lang="en">Latent Dirichlet Allocation (LDA)</dc:subject>
	<dc:subject xml:lang="en">Text Mining</dc:subject>
	<dc:subject xml:lang="en">Topic Modeling</dc:subject>
	<dc:description xml:lang="en">Detikcom is a very popular news portal today. The news on the portal continues to grow time to time, causing the existing news data to pile up. As a result, this is necessary to utilize this large amount of data. One of the ways that can be used is to extract topics from news text data through topic modeling using the Latent dirichlet allocation (LDA) method. This method is very popular because it can perform analysis on very large documents. This research aims to find certain patterns in a document by generating several different topics so that it does not specifically divide documents into a particular topic. This research has three topics obtained, with a coherence score is 0,7586. The first topic discusses conflicts and crises within a country, the second topic discusses issues related to humanitarian, and the third topic discusses the issues of corruption committed by state officials.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-02-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/24843</dc:identifier>
	<dc:identifier>10.20956/ejsa.vi.24843</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 1, Januari, 2023 : Estimasi; 53-63</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/24843/9333</dc:relation>
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