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Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 2 (2022) · pp. 113–124

DOI
10.22146/ijccs.68903
ISSN
2460-7258, 1978-1520
Published
2022-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.

Keywords

Collaboration network of the authors

22 authors · 42 co-authorship links · 15 across different organisations

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

  • Universitas Gadjah Mada
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Dian Nuswantoro
  • Udayana University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ghulam ImanuddinUniversitas Gadjah Mada1
Suci Karunia PrilistyaUniversitas Gadjah Mada1
Azhari SnUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Edi WinarkoUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Yohanes SuyantoUniversitas Gadjah Mada1
Khabib MustofaUniversitas Gadjah Mada1
Amalia Nur AnggraeniUniversitas Gadjah Mada1
Dika Permana PutraUniversitas Gadjah Mada1
Nur FadilahUniversitas Gadjah Mada1
Muthia Farah HanifaSyarif Hidayatullah State Islamic University Jakarta1
Anugrah Tri RamadhanUniversitas Dian Nuswantoro1
Ni’Matul HusnaUniversitas Gadjah Mada1
Nabila Apriliana WidiyonoUniversitas Gadjah Mada1
Rhamdan Syahrul MubarakUniversitas Gadjah Mada1
Adisti Anjani PutriUdayana University1

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 of KAI Access Reviews Using NBC and SVM</dc:title>
	<dc:creator>Mustakim, Huda</dc:creator>
	<dc:creator>Priyanta, Sigit</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">aspect-based sentiment analysis; kai access; support vector machine; naive bayes classifier</dc:subject>
	<dc:description xml:lang="en-US">The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.</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>2022-04-30</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/68903</dc:identifier>
	<dc:identifier>10.22146/ijccs.68903</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 2 (2022): April; 113-124</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/68903/33708</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>
</oai_dc:dc>