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Open access Jurnal SINTA 2

Sentiment Analysis Mobile JKN Reviews Using SMOTE Based LSTM

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 1 (2025) · pp. 13–24

DOI
10.22146/ijccs.101910
ISSN
2460-7258, 1978-1520
Published
2025-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The JKN Mobile application plays an important role in providing easy and fast access to health services for JKN-KIS users. However, user reviews indicate dissatisfaction with several aspects of the application, such as login issues and OTP codes, which can affect the overall user experience. Another challenge faced is class imbalance in the review dataset, which can affect the performance of sentiment analysis. This study uses Long Short-Term Memory (LSTM) combined with Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Review data was collected from Google Play Store and Kaggle, then preprocessed including lemmatization, tokenization, and padding. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that LSTM with SMOTE achieved 88% accuracy, 90% precision, 88% recall, and 89% F1-score. SMOTE successfully improved performance in the minority class although there was a slight decrease in accuracy compared to the model without SMOTE. Word cloud visualization reveals positive sentiments regarding the ease of use of the application, while negative sentiments indicate areas that need improvement. This study emphasizes the importance of handling imbalanced datasets to produce more accurate sentiment analysis.

Keywords

Collaboration network of the authors

5 authors · 6 co-authorship links

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  • Muria Kudus University
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  • Same organisation
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Co-authorOrganisationJoint articles
Muhammad ArifinMuria Kudus University1
Muhammad Taufiq HidayatMuria Kudus 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">Sentiment Analysis Mobile JKN Reviews Using SMOTE Based LSTM</dc:title>
	<dc:creator>Tamami, Ghufron</dc:creator>
	<dc:creator>Triyanto, Wiwit Agus</dc:creator>
	<dc:creator>Muzid, Syafiul</dc:creator>
	<dc:subject xml:lang="en-US">Information System; Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment Analysis; Imbalanced Data; LSTM; Mobile JKN; SMOTE</dc:subject>
	<dc:description xml:lang="en-US">The JKN Mobile application plays an important role in providing easy and fast access to health services for JKN-KIS users. However, user reviews indicate dissatisfaction with several aspects of the application, such as login issues and OTP codes, which can affect the overall user experience. Another challenge faced is class imbalance in the review dataset, which can affect the performance of sentiment analysis. This study uses Long Short-Term Memory (LSTM) combined with Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Review data was collected from Google Play Store and Kaggle, then preprocessed including lemmatization, tokenization, and padding. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that LSTM with SMOTE achieved 88% accuracy, 90% precision, 88% recall, and 89% F1-score. SMOTE successfully improved performance in the minority class although there was a slight decrease in accuracy compared to the model without SMOTE. Word cloud visualization reveals positive sentiments regarding the ease of use of the application, while negative sentiments indicate areas that need improvement. This study emphasizes the importance of handling imbalanced datasets to produce more accurate sentiment analysis.</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>2025-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/101910</dc:identifier>
	<dc:identifier>10.22146/ijccs.101910</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 1 (2025): January; 13-24</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/101910/40992</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 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>