Sistem Presensi Karyawan Berbasis Pengenalan Wajah Dengan Metode Support Vector Machine
Abstract
Sistem presensi saat ini yang ada pada instansi ataupun perusahaan masih banyak yang menggunakan sistem manual. Disisi lain, perusahaan-perusahaan tersebut juga telah memiliki aplikasi pengelolaan SDM online. Oleh karena itu, untuk efektifitas dan pengembangan sistem, perlu dilakukan pengembangan sistem presensi manual tersebut menjadi sebuah sistem yang dapat diintegrasikan dengan sistem pengelolaan SDM. Untuk itu, penelitian ini mengusulkan pengembangan sistem presensi berbasiskan pengenalan wajah yang diintegrasikan dengan aplikasi pengelolaan SDM. Sistem yang dibangun merupakan sistem deteksi dan pengenalan menggunakan Support Vector Machine yang di kombinasikan dengan metode Histogram of oriented gradient. Hasil pengujian sistem presensi menunjukkan hasil recall sebesar 77,78%, nilai spesifitas 32,22%, akurasi sistem 72,78%, dan kepresisian sistem mencapai 70,71%.
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References
E. Rahmawati et al., “Digital signature on file using biometric fingerprint with fingerprint sensor on smartphone,” Proceedings IES-ETA 2017 - International Electronics Symposium on Engineering Technology and Applications, vol. 2017-December, pp. 234–238, Dec. 2017, doi: 10.1109/ELECSYM.2017.8240409.
W. Jiang, X. Wang, X. Song, Q. Liu, and X. Liu, “Tracking your browser with high-performance browser fingerprint recognition model,” China Communications, vol. 17, no. 3, pp. 168–175, Mar. 2020, doi: 10.23919/JCC.2020.03.014.
B. P. Nguyen, W. L. Tay, and C. K. Chui, “Robust Biometric Recognition from Palm Depth Images for Gloved Hands,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 6, pp. 799–804, Dec. 2015, doi: 10.1109/THMS.2015.2453203.
A. Gangwar and A. Joshi, “DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition,” undefined, vol. 2016-August, pp. 2301–2305, Aug. 2016, doi: 10.1109/ICIP.2016.7532769.
R. He, X. Wu, Z. Sun, and T. Tan, “Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1761–1773, Jul. 2019, doi: 10.1109/TPAMI.2018.2842770.
S. Wardoyo, R. Wiryadinata, and R. Sagita, “Sistem Presensi Berbasis Agoritma Eigenface Dengan Metode Principal Component Analysis,” Setrum: Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 3, no. 1, pp. 61–68, Mar. 2016, doi: 10.36055/SETRUM.V3I1.498.
Hanif Al Fatta, Rekayasa Sistem Pengenalan Wajah. Yogyakarta: Andi Offset, 2009.
Ari Kurniawan and Marzuki Syahfirin, “Aplikasi Deteksi Objek Menggunakan Histogram of Oriented Gradient Untuk Modul Sistem Cerdas Pada Robot Nao,” Lampung, 2016.
A. Zisserman, “C19 Machine Learning lectures Hilary 2015, Lecture 2: The SVM classifier.” 2015.
C. Cortes, V. Vapnik, and L. Saitta, “Support-vector networks,” Machine Learning 1995 20:3, vol. 20, no. 3, pp. 273–297, Sep. 1995, doi: 10.1007/BF00994018.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June-2015, pp. 815–823, Mar. 2015, doi: 10.1109/CVPR.2015.7298682.
Emmanuel Agu, “Digital Image Processing (CS/ECE 545) Lecture 11: Geometric Operations, Comparing Images and Future Directions.” Accessed: Dec. 29, 2021. [Online]. Available: https://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/slides/lecture01.pdf
Darma Putra Westriningsih, Pengolahan citra digital / Darma Putra, 1st ed. Yogyakarta: Andi, 2010.
D. M. W. Powers and Ailab, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” Oct. 2020, Accessed: Dec. 28, 2021. [Online]. Available: https://arxiv.org/abs/2010.16061v1
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