ANALISIS PENGGUNA APLIKASI JAMSOSTEK MOBILE MENGGUNAKAN METODE NAÏVE BAYES BERBASIS DATA ULASAN DI PLAY STORE

Ainur Rohim Pratama; Bambang Irawan; Ahmad Faqih; Martanto .; Umi Hayati

Authors

Ainur Rohim Pratama STMIK IKMI Cirebon, Indonesia
Bambang Irawan STMIK IKMI Cirebon, Indonesia
Ahmad Faqih STMIK IKMI Cirebon, Indonesia
Martanto . STMIK IKMI Cirebon, Indonesia
Umi Hayati STMIK IKMI Cirebon, Indonesia

Keywords:

Analisis Sentimen, Algoritma Naïve Bayes, Google Play Store

Abstract

Perkembangan aplikasi digital telah memudahkan masyarakat dalam mengakses layanan publik, termasuk aplikasi Jamsostek Mobile (JMO) dari BPJS Ketenagakerjaan. Namun, ulasan pengguna terhadap aplikasi ini di Google Play Store menunjukkan berbagai sentimen yang perlu dianalisis untuk meningkatkan kualitas layanan. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi JMO menggunakan algoritma Naïve Bayes. Sebanyak 3000 ulasan dikumpulkan menggunakan metode web scraping, kemudian dilakukan tahapan preprocessing seperti tokenisasi, normalisasi, dan stemming. Hasil evaluasi menunjukkan bahwa model Bernoulli Naïve Bayes mampu mengklasifikasikan sentimen dengan akurasi 90,62%, precision 0,97 untuk sentimen negatif dan recall 0,98 untuk sentimen positif. Wordcloud memperlihatkan kata kunci dominan seperti "login", "error", dan "bagus", mengindikasikan aspek teknis sebagai perhatian utama pengguna. Sistem klasifikasi ini juga diimplementasikan dalam bentuk prototipe berbasis web untuk mempermudah monitoring opini publik. Penelitian ini memberikan kontribusi pada penerapan Machine Learning dalam klasifikasi sentimen teks berbahasa Indonesia, khususnya pada aplikasi layanan publik.

References

C. K. K. Bharathi and K. Elakkiyan, “Leveraging artificial intelligence for predictive modelling of consumer buying intentions on E-Commerce platforms,” Discov. Artif. Intell., vol. 6, no. 1, 2026, doi: 10.1007/s44163-025-00815-7.

S. Sidra and S. M. Wagan, “How artificial intelligence enabled marketing activities will change consumer behavior,” Discov. Artif. Intell., vol. 6, no. 1, 2026, doi: 10.1007/s44163-025-00735-6.

A. Mendieta-Aragón and T. Garín-Muñoz, “Consumer behaviour in e-Tourism: Exploring new applications of machine learning in tourism studies,” Investig. Tur., no. 26, pp. 350–374, 2023, doi: 10.14198/INTURI.24629.

S. Kim, W. Shin, and H.-W. Kim, “Predicting online customer purchase: The integration of customer characteristics and browsing patterns,” Decis. Support Syst., vol. 177, 2024, doi: 10.1016/j.dss.2023.114105.

B. Alsulami, B. Alwated, K. Barashid, M. Abdullah, M. Alosaimi, and S. Alhusayni, “ON LEVERAGING GENERATIVE ARTIFICIAL INTELLIGENCE (GENAI) FOR BEHAVIOR LEARNING AND PERSONALIZED MARKETING OPTIMIZATION,” Arch. Tech. Sci., vol. 17, no. 34, pp. 35–58, 2025, doi: 10.70102/afts.2025.1834.035.

Y. Shen Gu, D., Chen, F., Zhang, Z., & Liu, G., “Big data analytics and healthcare management: A systematic review of applications and performance outcomes,” Int. J. Med. Informatics, 164, 104849, 2022, doi: 10.1016/j.ijmedinf.2022.104849.

N. Wijemunige, S. Silva, and R. Perera, “Population-based healthcare utilization modeling to improve outpatient screening and preventive care services,” BMC Public Health, vol. 22, no. 1, p. 1847, 2022, doi: 10.1186/s12889-022-14152-9.

A. R. Shour, G. L. Jones, R. Anguzu, S. A. Doi, and A. A. Onitilo, “Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system,” BMC Health Serv. Res., vol. 23, no. 1, p. 1085, 2023, doi: 10.1186/s12913-023-09969-5.

C. P. Mullan, R. O’Sullivan, and E. McCarthy, “Reducing non-attendance in pediatric outpatient services through communication improvement interventions,” BMJ Open Qual., vol. 13, no. 1, p. e002553, 2024, doi: 10.1136/bmjoq-2023-002553.

H. J. M. Schreurs, M. van den Akker, and M. W. J. Jansen, “Delayed care-seeking and attendance challenges in obesity outpatient treatment pathways: A qualitative study,” Int. J. Obes., vol. 49, no. 2, pp. 245–254, 2025, doi: 10.1038/s41366-024-01625-7.

J. M. Fiszdon, “Patient engagement, attendance, and outcomes in community outpatient cognitive training programs: A randomized controlled trial,” Psychiatr. Rehabil. J., 2024, doi: 10.1037/prj0000568.

S. Matimbwa, G. Mshana, and B. A. Knettel, “Barriers and facilitators of retention in HIV outpatient care: A qualitative study in Tanzania,” BMC Health Serv. Res., vol. 25, no. 1, p. 214, 2025, doi: 10.1186/s12913-025-12140-2.