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Factor analysis influencing Mobile JKN user experience using sentiment analysis

IAES International Journal of Artificial Intelligence · Vol. 13 No. 2 (2024) · pp. 1782–1793

DOI
10.11591/ijai.v13.i2.pp1782-1793
ISSN
2252-8938, 2089-4872
Published
2024-06-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.

Keywords

Collaboration network of the authors

27 authors · 61 co-authorship links · 13 across different organisations

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

  • University of Indonesia
  • Badan Pusat Statistik
  • Ministry of Finance
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hasna Melani PuspasariAffiliation unknown2
Ilham Zharif MustaqimAffiliation unknown2
Avita Tri UtamiAffiliation unknown2
Rahmad SyaleviAffiliation unknown2
Mohamad Faisal ZulmyMinistry of Finance1
Monica Vivi KurniawatiAffiliation unknown1
Siti Mawar Rini WintangBadan Pusat Statistik1
Joko ParmiyantoBadan Pusat Statistik1
Freddy Richard Putra HuluUniversity of Indonesia1
Azanisa PutriUniversity of Indonesia1
Rifta Dimas SulistiyoUniversity of Indonesia1
Tito Febrian NugrahaUniversity of Indonesia1
Wahyu Setiawan WibowoBadan Pusat Statistik1
Venera GeniaUniversity of Indonesia1
Ahmad FadhilUniversity of Indonesia1
Yohan Adhi StyoutomoAffiliation unknown1
Puja Putri AbdullahUniversity of Indonesia1
Rina RahmawatiUniversity of Indonesia1
Fathurahman Ma'ruf HudoarmaAffiliation unknown1
Rezki KhairunnasAffiliation unknown1
Jeri Apriansyah PaguaAffiliation unknown1
Ghina FitriyaAffiliation unknown1

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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Factor analysis influencing Mobile JKN user experience using sentiment analysis</dc:title>
	<dc:creator>Al Qahar, Muhammad Yazid</dc:creator>
	<dc:creator>Ruldeviyani, Yova</dc:creator>
	<dc:creator>Mukharomah, Ulfah Nur</dc:creator>
	<dc:creator>Fidyawan, Miftahul Agtamas</dc:creator>
	<dc:creator>Putra, Ramadhoni</dc:creator>
	<dc:subject xml:lang="en-US">Natural language processing; Sentiment Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Mobile JKN; Naïve Bayes; Online service quality factor; Sentiment analysis; SMOTE; Support vector machine;</dc:subject>
	<dc:description xml:lang="en-US">Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">BPJS Kesehatan</dc:contributor>
	<dc:contributor xml:lang="en-US">Master of Information Technology, Faculty of Computer Science</dc:contributor>
	<dc:date>2024-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23486</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i2.pp1782-1793</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 1782-1793</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23486/13982</dc:relation>
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	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/23486/3337</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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