Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Sentiment Classification of MyTelkomsel Reviews Using SVM and Logistic Regression

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 1 (2026) · pp. 83–94

DOI
10.22146/ijccs.110409
ISSN
2460-7258, 1978-1520
Published
2026-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The development of digital technology has encouraged increased user participation in expressing opinions through review platforms, such as the Google Play Store. MyTelkomsel's application, a digital service from Indonesia's leading telecommunications provider, has received various responses, from appreciation to complaints related to app performance and customer service. This study aims to evaluate sentiment in user reviews using Support Vector Machine (SVM) and Logistic Regression algorithms. Data was collected from the Google Play Store and underwent a series of pre-processing stages, including data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The feature extraction process uses the TF-IDF approach, while model performance evaluation is based on accuracy, precision, recall, F1-score, and Area Under Curve (AUC) metrics. The results showed that the performance of both models was relatively balanced, but SVM exhibited an advantage in recall for positive sentiment (82%), accuracy (93.36%), and AUC (0.9680). Logistic Regression excels in precision (99%) in the positive class. WordCloud visualization illustrates consistency of dominant words in each sentiment class, reflecting the model's ability to identify patterns in user opinion. These findings are expected to contribute to the improvement of digital services based on user input.

Keywords

Collaboration network of the authors

3 authors · 3 co-authorship links

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

  • Muria Kudus University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Sentiment Classification of MyTelkomsel Reviews Using SVM and Logistic Regression</dc:title>
	<dc:creator>Adinata, Rijal Bagus</dc:creator>
	<dc:creator>Supriyono, Supriyono</dc:creator>
	<dc:creator>Fithri, Diana Laily</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment Analysis; MyTelkomsel; Logistic Regression; Support Vector Machine; TF-IDF</dc:subject>
	<dc:description xml:lang="en-US">The development of digital technology has encouraged increased user participation in expressing opinions through review platforms, such as the Google Play Store. MyTelkomsel's application, a digital service from Indonesia's leading telecommunications provider, has received various responses, from appreciation to complaints related to app performance and customer service. This study aims to evaluate sentiment in user reviews using Support Vector Machine (SVM) and Logistic Regression algorithms. Data was collected from the Google Play Store and underwent a series of pre-processing stages, including data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The feature extraction process uses the TF-IDF approach, while model performance evaluation is based on accuracy, precision, recall, F1-score, and Area Under Curve (AUC) metrics. The results showed that the performance of both models was relatively balanced, but SVM exhibited an advantage in recall for positive sentiment (82%), accuracy (93.36%), and AUC (0.9680). Logistic Regression excels in precision (99%) in the positive class. WordCloud visualization illustrates consistency of dominant words in each sentiment class, reflecting the model's ability to identify patterns in user opinion. These findings are expected to contribute to the improvement of digital services based on user input.</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>2026-01-31</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/110409</dc:identifier>
	<dc:identifier>10.22146/ijccs.110409</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 1 (2026): January; 83-94</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/110409/43432</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 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>