Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent Dirichlet Allocation

Matira, Yayang; Junaidi; Setiawan, Iman

doi:10.20956/ejsa.vi.24843

Jurnal

Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent Dirichlet Allocation

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

DOI: 10.20956/ejsa.vi.24843
ISSN: 2721-379X, 2721-3803
Terbit: 2023-02-14
Penerbit: Hasanuddin University
Bahasa: en

Abstrak

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.

Kata kunci

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13 penulis · 34 tautan kepenulisan bersama · 9 lintas organisasi

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Tadulako University
North Sumatra Islamic University
Institut Teknologi Sepuluh Nopember
Afiliasi tidak diketahui

Organisasi berbeda
Organisasi sama
Afiliasi tidak diketahui
Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel

Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Fadjryani Fadjryani	Tadulako University	3
Junaidi Junaidi	North Sumatra Islamic University	1
Mohammad Batara	Tadulako University	1
Syaiful Hendra	Tadulako University	1
Nurmasita Ismail	Afiliasi tidak diketahui	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

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, pertama di-harvest 2 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<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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