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CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 2 (2023) · pp. 0847–0854

DOI
10.30598/barekengvol17iss2pp0847-0854
ISSN
2615-3017, 1978-7227
Published
2023-06-11
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.

Collaboration network of the authors

24 authors · 54 co-authorship links

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  • Binus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sutanto SutantoAffiliation unknown6
Purnami WidyaningsihAffiliation unknown5
Antoni WibowoBinus University3
Heri PrasetyoAffiliation unknown2
Fadiah KhairinaAffiliation unknown2
Gusti Ngurah Adhi WibowoAffiliation unknown2
Harun Al RasyidAffiliation unknown2
Husnun Nur Ghiffari Putri RiyansyahAffiliation unknown1
Bowo WinarnoAffiliation unknown1
Ria Puan AdhitamaAffiliation unknown1
Andjani Ayu Cahaya TanjungAffiliation unknown1
Nughthoh Arfawi KurdhiAffiliation unknown1
Muhammad Nazhif Abda Putera KhanoAffiliation unknown1
Alexander Yonathan ChristyAffiliation unknown1
Aslina BaharumAffiliation unknown1
Siti Roqhilu YumarohAffiliation unknown1
Sela Putri IndriatiAffiliation unknown1
Andre Fajry Al BarraAffiliation unknown1
Ghufron Musta'inAffiliation unknown1
Fahri Aimar KusumoAffiliation unknown1
Ghaitsa Shafa Cinta KanantaAffiliation unknown1
Nicolas Ray Amarco TambunanAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION</dc:title>
	<dc:creator>Saputro, Dewi Retno Sari</dc:creator>
	<dc:creator>Sidiq, Krisna</dc:creator>
	<dc:description xml:lang="en-US">The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-06-11</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7819</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss2pp0847-0854</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0847-0854</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss2year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7819/5946</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Dewi Retno Sari Saputro, Krisna Sidiq</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>