IMPLEMENTASI AUTENTIKASI MULTI-FAKTOR DENGAN PENGENALAN WAJAH PADA PT. TRASPAC MAKMUR SEJAHTERA

• RIZKY ATMAWIJAYA

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ABSTRAK

ABSTRAK

Rizky Atmawijaya (12220228), Implementasi Autentikasi Multi-Faktor dengan Pengenalan Wajah pada PT. Traspac Makmur Sejahtera.

Layanan digital berbasis online adalah aset yang harus dilindungi. Terutama jika aplikasi tersebut masih menggunakan autentikasi satu-faktor yang rentan terhadap serangan siber dan berpotensi menyebabkan kebocoran data dan pencurian identitas. Solusi yang diusulkan adalah implementasi autentikasi multi-faktor (MFA) dengan memanfaatkan pengenalan wajah, terutama FaceNet. Meskipun pengenalan wajah menawarkan keamanan tambahan, tantangan utama adalah tetap menjaga privasi pengguna meskipun informasi biometrik bocor. Penelitian ini bertujuan menciptakan model MFA yang aman, andal, dan melindungi privasi karyawan PT. Traspac Makmur Sejahtera. Metode yang diusulkan adalah sistem MFA yang terdiri dari empat faktor, yaitu faktor pengetahuan (password), faktor biometrik (pengukuran wajah), faktor kepemilihan (OTP), dan faktor lokasi. Faktor lokasi hanya faktor opsional jika akurasi wajah tidak akurat. Hasil dari implementasi model MFA ini meningkatkan keamanan, andal, dan melindungi privasi karyawan. Dengan mempertimbangkan kebutuhan khusus perusahaan, penelitian ini bisa membantu perusahaan dalam memonitor lokasi karyawan yang izin WFH.

Kata kunci: Autentikasi Multi Faktor (MFA), Pengenalan Wajah, FaceNet, Melindungi Privasi, Deep Learning

KATA KUNCI

Autentikasi Multi Faktor (MFA),Teknologi Pengenalan Wajah,FaceNet,Deep Learning,Melindungi Privasi

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

[1] S. P. Otta, S. Panda, M. Gupta, and C. Hota, “A Systematic Survey of MultiFactor Authentication for Cloud Infrastructure,” Future Internet, vol. 15, no. 4, Apr. 2023, doi: 10.3390/fi15040146.

[2] T. Y. Xin, N. Katuk, and A. S. C. M. Arif, “Smart Home Multi-Factor Authentication Using Face Recognition and One-Time Password on Smartphone,” International Journal of Interactive Mobile Technologies, vol. 15, no. 24, pp. 32–48, 2021, doi: 10.3991/IJIM.V15I24.25393.

[3] J. Williamson and K. Curran, “The Role of Multi-factor Authentication for Modern Day Security,” Semiconductor Science and Information Devices, vol. 3, no. 1, May 2021, doi: 10.30564/ssid.v3i1.3152.

[4] A. K. Datta, M. Datta, and P. K. Banerjee, Face detection and recognition: Theory and practice. CRC Press, 2015. doi: 10.1201/b19349.

[5] I. Masi, A. T. Tr?n, T. Hassner, G. Sahin, and G. Medioni, “Face-Specific Data Augmentation for Unconstrained Face Recognition,” Int J Comput Vis, 2019, doi: 10.1007/s11263-019-01178-0.

[6] A. Ometov, S. Bezzateev, N. Mäkitalo, S. Andreev, T. Mikkonen, and Y. Koucheryavy, “Multi-factor authentication: A survey,” Cryptography, vol. 2, no. 1, pp. 1–31, Mar. 2018, doi: 10.3390/cryptography2010001.

[7] Z. Rui and Z. Yan, “A Survey on Biometric Authentication: Toward Secure and Privacy-Preserving Identification,” IEEE Access, vol. 7. Institute of Electrical and Electronics Engineers Inc., pp. 5994–6009, 2019. doi: 10.1109/ACCESS.2018.2889996.

[8] I. Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, “Past, present, and future of face recognition: A review,” Electronics (Switzerland), vol. 9, no. 8. MDPI AG, pp. 1–53, Aug. 01, 2020. doi: 10.3390/electronics9081188.

[9] Cahyono F, “Pengenalan Wajah Menggunakan Model FaceNet untuk Presensi Pegawai,” INSTITUT TEKNOLOGI SEPULUH NOPEMBER, 2020.

[10] M. Taskiran, N. Kahraman, and C. E. Erdem, “Face recognition: Past, present and future (a review),” Digital Signal Processing: A Review Journal, vol. 106. Elsevier Inc., Nov. 01, 2020. doi: 10.1016/j.dsp.2020.102809.

[11] M. D. Zeiler and R. Fergus, “LNCS 8689 - Visualizing and Understanding Convolutional Networks,” 2014.

[12] R. Jin, H. Li, J. Pan, W. Ma, and J. Lin, “Face Recognition Based on MTCNN and FaceNet,” 2021, [Online]. Available: www.aaai.org lxxxi

[13] V. Maheswari, C. A. Sari, D. R. I. M. Setiadi, and E. H. Rachmawanto, “Face Recognition using FaceNet (Survey, Performance Test, and Comparison),” in Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020, Institute of Electrical and Electronics Engineers Inc., Sep. 2020, pp. 55–60. doi: 10.1109/iSemantic50169.2020.9234250.

[14] “Privacy Principles for Facial Recognition Technology in Commercial Applications,” 2018.

[15] D. Z. and G. D. S. J. Morris Chang, Privacy-Preserving Machine Learning, First Edition. Manning Publications, 2023.

[16] A. Majeed and S. Lee, “Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey,” IEEE Access, vol. 9, pp. 8512–8545, 2021, doi: 10.1109/ACCESS.2020.3045700.

[17] P. Anthony, B. Ay, and G. Aydin, “A review of face anti-spoofing methods for face recognition systems,” in 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Aug. 2021. doi: 10.1109/INISTA52262.2021.9548404.

[18] Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep Tree Learning for Zeroshot Face Anti-Spoofing,” in IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA: IEEE Xplore, 2019. doi: 10.1109/CVPR.2019.00481.

[19] G. Ali, “Development of a secure multi-factor authentication algorithm for mobile money applications,” 2023. [Online]. Available: https://dspace.nmaist.ac.tz/handle/20.500.12479/2210

[20] Prabowo M, Metodologi Pengembangan Sistem Informasi. LP2M Press IAIN Salatiga, 2020.

[21] Roger S. Pressman and B. Maxim, Software Engineering: A Practitioner’s Approach, 9th ed. 2023. [Online]. Available: www.mhhe.com/pressman.

[22] M. Aniche, Effective and systematic software testing : A developer’s guide. Manning Publications, 2022.

[23] R. A. Sukamto and M. Shalahuddin, Rekayasa Perangkat Lunak, 4th ed. Informatika, 2016.