KEKHAWATIRAN PRIVASI DATA DAN KEPERCAYAAN PENGGUNA MOBILE JKN PASCA KEBOCORAN DATA BPJS KESEHATAN: PERSPEKTIF PROTECTION MOTIVATION THEORY
DOI:
https://doi.org/10.31849/anxqez63Keywords:
Protection Motivation Theory, Mobile JKN, Privacy Concern, Kebocoran Data, TrustAbstract
Kebocoran data BPJS Kesehatan pada tahun 2021 yang diduga melibatkan sekitar 279 juta data penduduk Indonesia menimbulkan pertanyaan mengenai bagaimana persepsi risiko privasi memengaruhi niat pengguna untuk tetap menggunakan aplikasi Mobile JKN. Penelitian ini bertujuan menganalisis pengaruh perceived severity, perceived vulnerability, response efficacy, self-efficacy, response cost, privacy concern, dan trust terhadap Behavioral intention pengguna Mobile JKN dengan pendekatan Protection Motivation Theory (PMT) yang diperluas. Data dikumpulkan dari 210 responden pengguna Mobile JKN di Indonesia melalui kuesioner daring dan dianalisis menggunakan metode Partial Least Square Structural Equation Modeling (PLS-SEM) dengan bantuan SmartPLS 4. Hasil penelitian menunjukkan bahwa Perceived vulnerability berpengaruh negatif dan signifikan, sedangkan trust berpengaruh positif dan signifikan terhadap Behavioral intention, dengan trust sebagai faktor paling dominan dalam model, sedangkan perceived severity, response efficacy, self-efficacy, response cost, dan privacy concern tidak terbukti berpengaruh signifikan. Penelitian ini diharapkan dapat memberikan kontribusi teoretis bagi pengembangan model PMT pada konteks layanan publik digital, serta kontribusi praktis bagi BPJS Kesehatan dalam merumuskan strategi peningkatan keamanan dan kepercayaan pengguna terhadap Mobile JKN.
References
[1] Febrianti, Z. S., & Devega, M. Analisis Sentimen Terhadap Penggunaan Aplikasi Mobile Jkn Dengan Pendekatan Random Forest Classifier, ZONAsi, 2025, 7(3), 1081–1091.
[2] Ginting, R., Asdar, J., Samura, P., Parinduri, A. I., Br, S. M., & Hasanah, D. N. Hubungan Pemanfaatan Aplikasi Mobile Jaminan Kesehatan Nasional ( JKN ) dengan Pelayanan Kesehatan, JKF,2024;c, 97–101.
[3] Nugraha, D. A., Nurfitroh, R., Ul-Haq, N. D., Dika, R. P., & Lagontang, S. N.. Kebocoran Data Bpjs Kesehatan : Ancaman Terhadap Keamanan Informasi Publik Di Era DigitaL, IPSSJ, 2021, 2(3), 4685–4691.
[4] Munandar, A., Listiani, D., Malik, F. A., Pratama, B. A., & Elfina, A. Kebocoran Data BPJS sebagai Studi Kasus Kelemahan Keamanan Lembaga Publik, TEKNOBIS, 2025, 3(2), 2023–2026.
[5] Sorisa, C., & Kiareni, C. L. Etika Keamanan Siber: Studi Kasus Kebocoran Data BPJS Kesehatan di Indonesia Cinda. Jurnal Sains Student Research, 2024, Vol.2, 2(6), 586–593.
[6] Kusumawati, R. M., Wulandari, K., Masyarakat, K., Masyarakat, F. K., Widya, U., Mahakam, G., Wahid, J., & No, H. JKN Mobile Application Utilization. Jurnal Kebijakan Kesehatan Indonesia (JKKI), 2024, 13(01), 52–55.
[7] Meier, Y., & Krämer, N. C. A longitudinal examination of Internet users ’ privacy protection behaviors in relation to their perceived collective value of privacy and individual privacy concerns, Article reuse guidelines, 2024, Vol. 26(10) 5942 –5961
[8] Setiawan, F. S. Analisis kepercayaan pengguna TikTok terhadap sistem rekomendasi tiktok yang berbasis Artificial Intelligence ( AI ) dengan model Trust In AI,Academia.Edu, 2024, 240160121134.
[9] D., M., & S., P. Protection Motivation Theory: A review. In S. Papagiannidis (Ed), TheoryHub Book, 2025.
[10] Rogers W., R. Cognitive and physiological processes in fear appeals and attitude change: a revised theory of protection motivation. Social Psychophysiology: A Sourcebook, January 1983, 153–177.
[11] Syafiqah, F., Pinandito, A., & Rokhmawati, R. I. Analisis Kesediaan User Permission pada Fitur App Tracking Transparency melalui pendekatan Protection Motivation Theory. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Januari 2017, 1(1), 1–9.
[12] Putri, D. Y., Suratno, T., & Noverina, Y. Kesadaran Privasi Data Dan Kepercayaan Pengguna Aplikasi Seabank Menggunakan Protection Motivation Theory. Jati, 2025, 9(4), 6207–6214.
[13] Rogers W., R. Cognitive and physiological processes in fear appeals and attitude change: a revised theory of protection motivation. Social Psychophysiology: A Sourcebook, January 1983, 153–177.
[14] Untari, D. T. Metodologi Penelitian. In Pena Persada, Banyumas, Jawa Tengah, Indonesia.CV.Perna Persada, 2018
[15] Hair, J, F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. A Primer on particial least squaing res Structual Equation Modeling (PLS-SEM). In European Journal of Tourism Research, 2022, (Vol. 6, Issue 2, pp. 211–213).
[16] Workman, M., Bommer, W. H., & Straub, D,. Computers in Human Behavior Security lapses and the omission of information security measures : A threat control model and empirical test, 2008 24, 2799–2816.
[17] Vance, A., Siponen, M., & Pahnila, S. Motivating IS security compliance: Insights from Habit and Protection Motivation Theory. Elsevier, 2012 49, 190–192.
[18] Yarsasi, S., Tahyudin, I., & Hariguna, T. Analisis Validitas dan Reliabilitas Kuesioner dengan Metode Partial Least Squares Structural Equation Modeling pada Aplikasi SMARTPLS Analysis of Validity and Reliability of Questionnaires Using Partial Least Squares Structural Equation Modeling Method on. 2025, 5(7), 1905–1913.
[19] Sanaky, M. M., Saleh, L. M., & Titaley, D. H. Uji Reabilitas. Jurnal Simetrik, 2021, 11(1), 432–439.
[20] Hair, J. F., Hult, G. T., Ringle, C., & Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) - Joseph F. Hair, Jr., G. Tomas M. Hult, Christian Ringle, Marko Sarstedt. In Sage, 2017
[21] Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. Partial Least
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