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Open access Jurnal SINTA 2

Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 4 (2022) · pp. 423–434

DOI
10.22146/ijccs.78664
ISSN
2460-7258, 1978-1520
Published
2022-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Improve the service quality of tourism actors by conducting sentiment analysis on digital platforms owned by tourism businesses and collecting negative sentiments to improve the quality of services from companies owned by tourism businesses. The growth of the hospitality industry in Indonesia is experiencing rapid growth every year. The tourism industry, part of the hospitality industry, also does not escape the influence of positive and negative sentiments. One method to perform accurate sentiment analysis is Backpropagation Neural Network. Based on the results of tests on the neural network, the best accuracy is obtained when using one hidden layer with the first layer of 10 neurons. The learning rate is 0.000002, where the accuracy is 71.630%. More epochs do not guarantee better accuracy. Based on the results of the research that has been done, suggestions for further researchers are to analyze the review dataset processing method so that it gets a cleaner dataset and is expected to improve better accuracy.

Keywords

Collaboration network of the authors

9 authors · 13 co-authorship links · 1 across different organisations

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

  • Institut Bisnis dan Teknologi Indonesia
  • Universitas Mercu Buana Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
I Made YudianaAffiliation unknown1
Paholo Iman PrakosoUniversitas Mercu Buana Yogyakarta1
I Kadek Agus DwipayanaInstitut Bisnis dan Teknologi Indonesia1
Ni Nengah Dita ArdrianiInstitut Bisnis dan Teknologi Indonesia1
Gede Agus SantiagoInstitut Bisnis dan Teknologi 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 Using Backpropagation Method to Recognize the Public Opinion</dc:title>
	<dc:creator>Wiguna, I Komang Arya Ganda</dc:creator>
	<dc:creator>Sugiartawan, Putu</dc:creator>
	<dc:creator>Sudipa, I Gede Iwan</dc:creator>
	<dc:creator>Pratama, I Putu Yudi</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science, Decision Support system</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment analysis; neural network; backpropagation</dc:subject>
	<dc:description xml:lang="en-US"> Improve the service quality of tourism actors by conducting sentiment analysis on digital platforms owned by tourism businesses and collecting negative sentiments to improve the quality of services from companies owned by tourism businesses. The growth of the hospitality industry in Indonesia is experiencing rapid growth every year. The tourism industry, part of the hospitality industry, also does not escape the influence of positive and negative sentiments. One method to perform accurate sentiment analysis is Backpropagation Neural Network. Based on the results of tests on the neural network, the best accuracy is obtained when using one hidden layer with the first layer of 10 neurons. The learning rate is 0.000002, where the accuracy is 71.630%. More epochs do not guarantee better accuracy. Based on the results of the research that has been done, suggestions for further researchers are to analyze the review dataset processing method so that it gets a cleaner dataset and is expected to improve better accuracy.</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>2022-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/78664</dc:identifier>
	<dc:identifier>10.22146/ijccs.78664</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 4 (2022): October; 423-434</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/78664/34992</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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