Melola Discovery

← Melola Discovery

Jurnal SINTA 2

OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 22 No. 2 (2024) · pp. 83–90

DOI
10.12962/j24068535.v22i2.a1204
ISSN
2406-8535, 1412-6389
Published
2024-07-31
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

YouTube has evolved into a globally influential platform, engaging over 2.1 billion users worldwide and serving as a prominent medium for sharing, consuming, and creating diverse video content. Particularly popular among younger demographics, YouTube stands as a multifaceted hub spanning various genres and has significantly impacted education by providing extensive educational materials, fostering independent learning, and supporting a wealth of educational resources. This research conducts an in-depth investigation into sentiment analysis specifically within the context of educational YouTube videos. Leveraging advanced machine learning techniques, notably RoBERTa, this research conducts a comparative analysis with Multinomial Naive Bayes (MNB). The primary focus is on exploring RoBERTa's adaptability and performance across a spectrum of educational video content, revealing its commendable accuracy of 91.21%, surpassing MNB's accuracy of 79.59%. However, it is observed that RoBERTa's performance is notably affected by smaller datasets, highlighting the critical importance of ample and diverse training data for achieving optimal results. These findings highlight the pivotal role of dataset characteristics and size in developing robust sentiment analysis models, especially with advanced natural language processing methods like RoBERTa.

Collaboration network of the authors

36 authors · 56 co-authorship links · 12 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • University of Brawijaya
  • Universitas Gadjah Mada
  • Mercu Buana University
  • Ghent University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Chastine FatichahInstitut Teknologi Sepuluh Nopember4
Achmad ArwanUniversity of Brawijaya2
Suhadi LiliInstitut Teknologi Sepuluh Nopember2
Dyah Sulistyowati RahayuInstitut Teknologi Sepuluh Nopember1
Bayu PriyambadhaUniversity of Brawijaya1
Aldy Sefan RezanaldyAffiliation unknown1
Abdul MunifInstitut Teknologi Sepuluh Nopember1
Vico Ade CandraAffiliation unknown1
Imam KuswardayanInstitut Teknologi Sepuluh Nopember1
Rahmi Ika NovianaAffiliation unknown1
Faizal JohanAffiliation unknown1
Widyanti KartikaAffiliation unknown1
Darlis Heru MurtiInstitut Teknologi Sepuluh Nopember1
Choiru ZainAffiliation unknown1
Kusbandono Ari BowoAffiliation unknown1
Lutfi Rizal GozaliAffiliation unknown1
Ratih Nur Esti AnggrainiInstitut Teknologi Sepuluh Nopember1
Mohammad Ahmaluddin ZinniInstitut Teknologi Sepuluh Nopember1
Fadli Husein WattiheluwInstitut Teknologi Sepuluh Nopember1
Lukman HakimInstitut Teknologi Sepuluh Nopember1
Divi Galih Prasetyo PutriUniversitas Gadjah Mada1
Muhammad Shulhan KhairyInstitut Teknologi Sepuluh Nopember1
Endang Wahyu PamungkasInstitut Teknologi Sepuluh Nopember1
Fernandes SinagaInstitut Teknologi Sepuluh Nopember1
Lukman HakimInstitut Teknologi Sepuluh Nopember1
Andrew Lomaksan Manuel TampubolonInstitut Teknologi Sepuluh Nopember1
Thio Marta Elisa Yuridis Butar ButarInstitut Teknologi Sepuluh Nopember1
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember1
Bambang JokonowoMercu Buana University1
Jan ClaesGhent University1
Achmad ArwanUniversity of Brawijaya1
Tiara Rahmania HadiningrumInstitut Teknologi Sepuluh Nopember1
Bella Dwi MardianaInstitut Teknologi Sepuluh Nopember1

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://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES</dc:title>
	<dc:creator xml:lang="en">Shabrina, Ulima Inas</dc:creator>
	<dc:creator xml:lang="en">Java, Muhammad Iskandar</dc:creator>
	<dc:creator xml:lang="en">Rochimah, Siti</dc:creator>
	<dc:description xml:lang="en">YouTube has evolved into a globally influential platform, engaging over 2.1 billion users worldwide and serving as a prominent medium for sharing, consuming, and creating diverse video content. Particularly popular among younger demographics, YouTube stands as a multifaceted hub spanning various genres and has significantly impacted education by providing extensive educational materials, fostering independent learning, and supporting a wealth of educational resources. This research conducts an in-depth investigation into sentiment analysis specifically within the context of educational YouTube videos. Leveraging advanced machine learning techniques, notably RoBERTa, this research conducts a comparative analysis with Multinomial Naive Bayes (MNB). The primary focus is on exploring RoBERTa's adaptability and performance across a spectrum of educational video content, revealing its commendable accuracy of 91.21%, surpassing MNB's accuracy of 79.59%. However, it is observed that RoBERTa's performance is notably affected by smaller datasets, highlighting the critical importance of ample and diverse training data for achieving optimal results. These findings highlight the pivotal role of dataset characteristics and size in developing robust sentiment analysis models, especially with advanced natural language processing methods like RoBERTa.</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2024-07-31</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/1204</dc:identifier>
	<dc:identifier>10.12962/j24068535.v22i2.a1204</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol. 22, No. 2, July 2024; 83-90</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v22i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/1204/512</dc:relation>
</oai_dc:dc>