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Aspect-Based Sentiment Analysis in Bromo Tengger Semeru National Park Indonesia Based on Google Maps User Reviews

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 1 (2023) · pp. 79–90

DOI
10.22146/ijccs.77354
ISSN
2460-7258, 1978-1520
Published
2023-02-23
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Technology can influence and shape a person's behavior patterns when planning tours, traveling, and after traveling. Visitors' reviews can be used as evaluation material to improve the quality of tourist destinations and become a determining factor for other tourists to visit or revisit the destinations. The process of utilizing these reviews can be done by assessing the aspects of tourist destinations based on reviews from visitors. This study aims to conduct an aspect-based sentiment analysis on one of the tourist destinations in Indonesia, namely Bromo Tengger Semeru National Park, based on reviews of Google Maps users. The aspects consist of attractions, facilities, access, and price. The sentiment classification model used is a machine learning model consisting of SVM, Complement Naïve Bayes, Logistic Regression, and transfer learning from pre-trained BERT, IndoBERT, and mBERT. Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 91.48% and 71.56%, respectively. In addition, among the machine learning models used, the SVM model gives the best results with an accuracy of 89.16% and an F1-Score of 62.23%.

Keywords

Collaboration network of the authors

6 authors · 6 co-authorship links

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  • Politeknik Statistika STIS
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Ibnu SantosoPoliteknik Statistika STIS1
Amanda Tabitha Bulan PanjaitanPoliteknik Statistika STIS1
Iftitah Athiyyah RahmaPoliteknik Statistika STIS1
Kharisma Ayu PradaniPoliteknik Statistika STIS1

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">Aspect-Based Sentiment Analysis in Bromo Tengger Semeru National Park Indonesia Based on Google Maps User Reviews</dc:title>
	<dc:creator>Bahri, Cynthia As</dc:creator>
	<dc:creator>Suadaa, Lya Hulliyyatus</dc:creator>
	<dc:subject xml:lang="en-US">Data Science</dc:subject>
	<dc:subject xml:lang="en-US">Aspect-Based Sentiment Analysis, Google Maps Review, Machine Learning, Transfer Learning</dc:subject>
	<dc:description xml:lang="en-US">Technology can influence and shape a person's behavior patterns when planning tours, traveling, and after traveling. Visitors' reviews can be used as evaluation material to improve the quality of tourist destinations and become a determining factor for other tourists to visit or revisit the destinations. The process of utilizing these reviews can be done by assessing the aspects of tourist destinations based on reviews from visitors. This study aims to conduct an aspect-based sentiment analysis on one of the tourist destinations in Indonesia, namely Bromo Tengger Semeru National Park, based on reviews of Google Maps users. The aspects consist of attractions, facilities, access, and price. The sentiment classification model used is a machine learning model consisting of SVM, Complement Naïve Bayes, Logistic Regression, and transfer learning from pre-trained BERT, IndoBERT, and mBERT. Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 91.48% and 71.56%, respectively. In addition, among the machine learning models used, the SVM model gives the best results with an accuracy of 89.16% and an F1-Score of 62.23%.</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>2023-02-23</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/77354</dc:identifier>
	<dc:identifier>10.22146/ijccs.77354</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 1 (2023): January; 79-90</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/77354/35783</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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