Sistem Absensi Mahasiswa Berbasis Dorsal Hand Vein Menggunakan Local Binary Patterns dan Fuzzy k-NN

  • Suharsono Bantun Universitas Sembilanbelas November Kolaka
  • Jayanti Yusmah Sari Universitas Sembilanbelas November Kolaka
  • Noorhasanah Z Universitas Sembilanbelas November Kolaka
  • Mardianto Mardianto Universitas Sembilanbelas November Kolaka
  • Aspian Achban Universitas Halu Oleo

Abstract

After WHO announced COVID-19 as a pandemic in 2020, universities began to implement online learning, after 1 year had passed, limited face-to-face learning would begin again by prioritizing the health and safety of campus residents. The offline lecture attendance system manually by signing the attendance sheet is at risk of becoming a medium for transmitting the virus because it is touched by many lecturers and students. Biometric systems are widely applied in various fields such as security systems and employee attendance. However, it has several weaknesses, such as the possibility of sabotage in fingerprints and palm geometry, difficulty in recognizing facial objects using accessories such as hats and glasses as well as changing expressions and expensive acquisition tools in retina-based recognition applications. This study uses Local Binary Patterns (LBP) to identify the dorsal hand vein. LBP is used as a feature extraction method to optimize the feature value of the vein texture in order to obtain good accuracy and fast processing speed. To match the dorsal vein features of the test image and the image in the database, the Fuzzy k-NN method is used. The test results show a good recognition accuracy of 90.67%.

Author Biographies

Jayanti Yusmah Sari, Universitas Sembilanbelas November Kolaka

Program Studi Ilmu Komputer, Fakultas Teknologi Informasi

Noorhasanah Z, Universitas Sembilanbelas November Kolaka

Program Studi Sistem Informasi, Fakultas Teknologi Informasi

Mardianto Mardianto, Universitas Sembilanbelas November Kolaka

Program Studi Sistem Informasi, Fakultas Teknologi Informasi

Aspian Achban, Universitas Halu Oleo

Program Studi Teknik Informatika, Fakultas Teknik

References

[1] A. Valerisha and M. A. Putra, “Pandemi Global Covid-19 Dan Problematika Negara-Bangsa: Transparansi Data Sebagai Vaksin Socio-Digital?,” J. Ilm. Hub. Int., vol. 0, no. 0, pp. 131–137, 2020, doi: 10.26593/jihi.v0i0.3871.131-137.
[2] S. H. D. Hatmo, “Dampak Pandemi Covid-19 Terhadap Efektivitas Pembelajaran Jarak Jauh Secara Daring,” Sch. J. Pendidik. dan Kebud., vol. 11, no. 2, pp. 115–122, 2021.
[3] N. B. Argaheni, “Sistematik Review: Dampak Perkuliahan Daring Saat Pandemi COVID-19 Terhadap Mahasiswa Indonesia,” PLACENTUM J. Ilm. Kesehat. dan Apl., vol. 8, no. 2, p. 99, 2020, doi: 10.20961/placentum.v8i2.43008.
[4] A. Harapani, “Pengaruh Kuliah Daring Saat Pandemi Covid-19 Terhadap Kemampuan Mahasiswa,” p. 8, 2020.
[5] Kementerian Pendidikan, Kebudayaan, Riset, Dan Teknologi. Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi. Surat Edaran Penyelenggaraan Pembelajaran Tatap Muka Tahun Akademik 2021/2022. 2021.
[6] N. V. De Lima, L. Novamizanti, and E. Susatio, “Sistem Pengenalan Wajah 3D Menggunakan ICP dan SVM,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 6, 2019.
[7] N. W. Marti and K. Y. E. Aryanto, “Prototipe Sistem Absensi Berbasis Face recognition Dengan Metode Eigenface,” Proceeding Semnasvoktek, vol. 1, p. 6, 2016.
[8] E. Indra, M. D. Batubara, M. Yasir, and S. Chau, “Desain dan Implementasi Sistem Absensi Mahasiswa Berdasarkan Fitur Pengenalan Wajah dengan Menggunakan Metode Haar-Like Feature: Sistem Informasi,” J. Teknol. Dan Ilmu Komput. Prima, vol. 2, no. 2, pp. 363–370, 2019.
[9] A. C. Rompas, S. Sompie, and A. Jacobus, “Aplikasi Absensi Berbasis Pangenalan wajah Multiple Person,” J. Tek. Inform., vol. 16, no. 2, pp. 129–136, 2021.
[10] J. C. Lee, C. H. Lee, C. B. Hsu, P. Y. Kuei, and K. C. Chang, “Dorsal hand vein recognition based on 2D Gabor filters,” Imaging Sci. J., vol. 62(3), pp. 127–138, 2014.
[11] M. Watanabe, Palm vein authentication. In Advances in biometrics. Springer London, 2008.
[12] N. C, C. Ashwini, A. Medha, N. Ramini, P. Kini, and S. K, “Biometric authentication by dorsal hand vein pattern,” Int. J. Eng. Technol., vol. 2(15), pp. 837–840, 2012.
[13] C. B. Hsu, J. C. Lee, S. J. Chuang, and P. Y. Kuei, “Gaussian directional pattern for dorsal hand vein recognition,” Imaging Sci. J., vol. 63(1), pp. 54–62.
[14] M. A. Rahim, H. N. Md, T. Wahid, and S. Azam, “Face Recognition using Local Binary Patterns (LBP),” Glob. J. Comput. Sci. Technol. Graph. Vis., 2013.
[15] F. J. Pontoh, J. Y. Sari, A. A. Ilham, and I. Nurtanio, “Multispectral Dorsal Hand Vein Recognition Based On Local Line Binary Pattern,” J. Ilmu Komput. dan Inf., Jun. 2018, doi: 10.21609/jiki.v11i2.576.
[16] P. I. Nainggolan and Y. Herdiyeni, “Aplikasi Mobile Untuk Identifikasi Tumbuhan Obat Menggunakan Local Binary Pattern Dengan Klasifikasi Probabilistic Neural Network,” Inst. Pertan. Bogor, pp. 1–15, 2013.
[17] J. Y. Sari, S. Bantun, and Z. Noorhasanah, “Local Binary Patterns for Dorsal Hand Vein Recognition,” pp. 235–239, 2021.
[18] J. M. Keller, M. R. Gray, and J. A. G. JR, “A fuzzy k-nearest neighbor algorithm,” IEEE Trans. Syst. Man. Cybern., vol. (4), pp. 580–585, 1985.
Published
2022-03-16