Jurnal Algoritma, Logika dan Komputasi

Near Field Communication (NFC) memungkinkan pembayaran nirkontak dilakukan secara cepat dan praktis, tetapi penerapannya pada dompet digital tetap membutuhkan mekanisme keamanan yang mampu mengenali transaksi tidak wajar selama proses pembayaran berlangsung. Penelitian ini mengimplementasikan aplikasi dompet digital berbasis NFC yang terintegrasi dengan Z-Score Based Anomaly Detection sebagai mekanisme deteksi fraud secara real-time. Sistem dikembangkan menggunakan React Native dan Expo Dev Client pada aplikasi mobile, Node.js dan Express.js pada backend, SQLite melalui Prisma ORM sebagai penyimpanan data, serta dashboard admin berbasis web untuk pemantauan. Proses deteksi fraud dilakukan dengan membandingkan transaksi baru terhadap dua puluh transaksi historis terakhir setiap pengguna. Nilai mean dan standar deviasi sampel dihitung dari baseline historis, kemudian nilai Z-Score digunakan untuk menentukan keputusan ALLOW, REVIEW, atau BLOCK. Pengujian dilakukan melalui simulasi manual, pengujian fungsional, pengujian keamanan, dan pengujian kompatibilitas perangkat Android. Hasil simulasi menunjukkan bahwa sistem mampu membedakan transaksi normal, mencurigakan, dan anomali. Andri memperoleh keputusan ALLOW dengan Z = 1,4142, Ibnu memperoleh REVIEW dengan Z = 2,6333, dan Adit memperoleh BLOCK dengan Z = 5,0332. Sistem juga mampu menangani kondisi khusus σ = 0 dengan mengizinkan transaksi identik dan memblokir transaksi yang berbeda dari pola historis. Hasil ini menunjukkan bahwa metode yang diterapkan ringan, transparan, dan relevan untuk keamanan pembayaran NFC pada tingkat prototipe.

P. Onumadu and H. Abroshan, “Near-field communication (NFC) cyber threats and mitigation solutions in payment transactions: A review,” Sensors, vol. 24, no. 23, p. 7423, 2024, doi: 10.3390/s24237423.

A. C. Dewi, E. I. H. Ujianto, and R. Rianto, “Electronic payment threats and security: A systematic literature review,” J. Nas. Pendidik. Tek. Inform., vol. 13, no. 2, 2024.

R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Stat. Sci., vol. 17, no. 3, pp. 235–255, 2002, doi: 10.1214/ss/1042727940.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv., vol. 41, no. 3, pp. 1–58, 2009, doi: 10.1145/1541880.1541882.

E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decis. Support Syst., vol. 50, no. 3, pp. 559–569, 2011.

P. Vanini, S. Rossi, E. Zvizdic, and T. Domenig, “Online payment fraud: From anomaly detection to risk management,” Financ. Innov., vol. 9, no. 1, p. 66, 2023, doi: 10.1186/s40854-023-00470-w.

Y. Kou, C. T. Lu, S. Sirwongwattana, and Y. P. Huang, “Survey of fraud detection techniques BT - IEEE International Conference on Networking, Sensing and Control,” 2004, pp. 749–754.

P. Vanini, S. Rossi, E. Zvizdic, and T. Domenig, “Online payment fraud: from anomaly detection to risk management,” Financ. Innov., vol. 9, no. 1, 2023, doi: 10.1186/s40854-023-00470-w.

Amelia Citra Dewi, Erik Iman Heri Ujianto, and R. Rianto, “Electronic Payment Threats and Security: A Systematic Literature Review,” J. Nas. Pendidik. Tek. Inform., vol. 13, no. 2, pp. 301–315, 2024, doi: 10.23887/janapati.v13i2.76635.

A. R. Hevner, S. T. March, J. Park, and S. Ram, “Design science in information systems research,” MIS Q., vol. 28, no. 1, pp. 75–105, 2004, doi: 10.2307/25148625.

EMVCo, “EMV Contactless Specifications for Payment Systems, Book C-8: Kernel 8 Specification, Version 1.1,” EMVCo, LLC, 2021.

N. X. P. Semiconductors, “NTAG213/215/216: NFC Forum Type 2 Tag IC with 144/504/888 bytes EEPROM: Data Sheet,” NXP Semiconductors, 2021. [Online]. Available: https://www.nxp.com/docs/en/data-sheet/NTAG213_215_216.pdf

I. Meta Platforms, “React Native Documentation: Learn Once, Write Anywhere,” 2023. [Online]. Available: https://reactnative.dev/docs/getting-started

I. Prisma Data, “Prisma ORM Documentation: Database Toolkit for Node.js and TypeScript,” 2023. [Online]. Available: https://www.prisma.io/docs

OpenStax, Introductory Statistics 2e: The Standard Normal Distribution. Rice University, 2023. [Online]. Available: https://openstax.org/books/introductory-statistics-2e

W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing, 3rd ed. Cambridge University Press, 2007.

D. C. Montgomery, Introduction to Statistical Quality Control, 7th ed. John Wiley & Sons, 2012.

O. Foundation, “OWASP Application Security Verification Standard,” OWASP Foundation, 2024. [Online]. Available: https://owasp.org/www-project-application-security-verification-standard/

S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decis. Support Syst., vol. 50, no. 3, pp. 602–613, 2011, doi: 10.1016/j.dss.2010.08.006.