SENTIMENT ANALYSIS ON E-SPORTS FOR EDUCATION CURRICULUM USING NAIVE BAYES AND SUPPORT VECTOR MACHINE

Ardianto, Rian; Rivanie, Tri; Alkhalifi, Yuris; Nugraha, Fitra Septia; Gata, Windu

doi:10.21609/jiki.v13i2.885

Jurnal SINTA 2

SENTIMENT ANALYSIS ON E-SPORTS FOR EDUCATION CURRICULUM USING NAIVE BAYES AND SUPPORT VECTOR MACHINE

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 13 No. 2 (2020) · pp. 109–122

DOI: 10.21609/jiki.v13i2.885
ISSN: 2502-9274, 2088-7051
Published: 2020-07-01
Publisher: Faculty of Computer Science - Universitas Indonesia
Language: eng

Abstract

The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum.

Keywords

Collaboration network of the authors

11 authors · 20 co-authorship links · 7 across different organisations

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Universitas Nusa Mandiri
Indonesian Institute of Sciences
National Research and Innovation Agency
Universitas Bina Sarana Informatika
Mercu Buana University
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
Basri Basri	Affiliation unknown	1
Risnandar Risnandar	Indonesian Institute of Sciences	1
Hilman Ferdinandus Pardede	National Research and Innovation Agency	1
Rousyati Rousyati	Universitas Nusa Mandiri	1
Dany Pratmanto	Universitas Bina Sarana Informatika	1
Nia Kusuma Wardhani	Mercu Buana 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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">SENTIMENT ANALYSIS ON E-SPORTS FOR EDUCATION CURRICULUM USING NAIVE BAYES AND SUPPORT VECTOR MACHINE</dc:title>
	<dc:creator>Ardianto, Rian</dc:creator>
	<dc:creator>Rivanie, Tri</dc:creator>
	<dc:creator>Alkhalifi, Yuris</dc:creator>
	<dc:creator>Nugraha, Fitra Septia</dc:creator>
	<dc:creator>Gata, Windu</dc:creator>
	<dc:subject xml:lang="en-US">Text Mining</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Naïve Bayes</dc:subject>
	<dc:subject xml:lang="en-US">Support Vector Machine</dc:subject>
	<dc:subject xml:lang="en-US">SMOTE.</dc:subject>
	<dc:description xml:lang="en-US">The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum.</dc:description>
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	<dc:date>2020-07-01</dc:date>
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	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 13 No. 2 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 109-122</dc:source>
	<dc:source>2502-9274</dc:source>
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	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/885/435</dc:relation>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/885/478</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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