Pemodelan Topik Artikel Berita Menggunakan Structural Topic Model dan Latent Dirichlet Allocation
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Authors:
Ayu Kadek Nadya Oktaviana, Ngurah Agus Sanjaya ER, Ida Bagus Made Mahendra, I Gede Santi Astawa, I Gede Arta Wibawa, I Komang Ari Mogi
Abstract:
“An online news portal is one of the technologies in the form of online media that provides information services in the form of news articles. The number of news articles on online news portals continues to grow over time and more news article data will be available. A large amount of data is a challenge in itself to be processed into a more useful form, namely by conducting topic modeling based on news article data so that the data can be categorized based on the topics discussed in it. Topic modeling groups text data into a specific set of topics based on their similarities. In this study, the dataset used was 44,425 news articles from November 2021 to March 2022 which were taken from the online news portal detik.com. News exploration was carried out by topic modeling using two methods, Latent Dirichlet Allocation (LDA) and Structural Topic Model (STM). The LDA method produces 8 topics derived from the calculation of the highest probabilistic coherence value. The STM method produces 11 topics based on the highest semantic coherence and exclusivity values.”
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https://jurnal.harianregional.com/jlk/full-88590
Published
2022-07-10
How To Cite
OKTAVIANA, Ayu Kadek Nadya et al. Pemodelan Topik Artikel Berita Menggunakan Structural Topic Model dan Latent Dirichlet Allocation.JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 11, n. 3, p. 469-478, july 2022. ISSN 2654-5101. Available at: https://ojs.unud.ac.id/index.php/JLK/article/view/88590. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JLK.2023.v11.i03.p02.
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Issue
Vol 11 No 3 (2023): JELIKU Volume 11 No 3, February 2023
Section
Articles
Copyright
This work is licensed under a Creative Commons Attribution 4.0 International License
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