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Open access Jurnal SINTA 2

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 1 (2021) · pp. 55–64

DOI
10.22146/ijccs.60718
ISSN
2460-7258, 1978-1520
Published
2021-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.

Keywords

Collaboration network of the authors

9 authors · 12 co-authorship links · 2 across different organisations

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

  • Universitas Teknokrat Indonesia
  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus SihabuddinUniversitas Gadjah Mada1
Yohanes SuyantoUniversitas Gadjah Mada1
Jepi SupriyantoUniversitas Teknokrat Indonesia1
Muhammad Pajar KharismaUniversitas Teknokrat Indonesia1
Ade Dwi PutraUniversitas Teknokrat Indonesia1
Dedi DarwisUniversitas Teknokrat Indonesia1

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://journal.ugm.ac.id/ijccs/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 Of Government Policy On Corona Case Using Naive Bayes Algorithm</dc:title>
	<dc:creator>Isnain, Auliya Rahman</dc:creator>
	<dc:creator>Marga, Nurman Satya</dc:creator>
	<dc:creator>Alita, Debby</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment analysis; coronavirus; new normal; Naive Bayes; Tf-IDF; and N-Gram</dc:subject>
	<dc:description xml:lang="en-US"> The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-01-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/60718</dc:identifier>
	<dc:identifier>10.22146/ijccs.60718</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 1 (2021): January; 55-64</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/60718/30558</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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