Factor analysis influencing Mobile JKN user experience using sentiment analysis

Al Qahar, Muhammad Yazid; Ruldeviyani, Yova; Mukharomah, Ulfah Nur; Fidyawan, Miftahul Agtamas; Putra, Ramadhoni

doi:10.11591/ijai.v13.i2.pp1782-1793

Akses terbuka Jurnal SINTA 1

Factor analysis influencing Mobile JKN user experience using sentiment analysis

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

DOI: 10.11591/ijai.v13.i2.pp1782-1793
ISSN: 2252-8938, 2089-4872
Terbit: 2024-06-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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27 penulis · 61 tautan kepenulisan bersama · 13 lintas organisasi

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University of Indonesia
Badan Pusat Statistik
Ministry of Finance
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Organisasi berbeda
Organisasi sama
Afiliasi tidak diketahui
Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel

Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Ilham Zharif Mustaqim	University of Indonesia	2
Hasna Melani Puspasari	University of Indonesia	2
Avita Tri Utami	University of Indonesia	2
Rahmad Syalevi	University of Indonesia	2
Mohamad Faisal Zulmy	Ministry of Finance	1
Monica Vivi Kurniawati	Afiliasi tidak diketahui	1
Siti Mawar Rini Wintang	Badan Pusat Statistik	1
Joko Parmiyanto	Badan Pusat Statistik	1
Freddy Richard Putra Hulu	University of Indonesia	1
Azanisa Putri	University of Indonesia	1
Rifta Dimas Sulistiyo	University of Indonesia	1
Tito Febrian Nugraha	University of Indonesia	1
Wahyu Setiawan Wibowo	Badan Pusat Statistik	1
Venera Genia	University of Indonesia	1
Ahmad Fadhil	University of Indonesia	1
Yohan Adhi Styoutomo	University of Indonesia	1
Puja Putri Abdullah	University of Indonesia	1
Rina Rahmawati	University of Indonesia	1
Fathurahman Ma'ruf Hudoarma	University of Indonesia	1
Rezki Khairunnas	University of Indonesia	1
Jeri Apriansyah Pagua	University of Indonesia	1
Ghina Fitriya	University of Indonesia	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (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>
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	<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>
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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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