IDENTIFIKASI CITRA IRIS MATA MENGGUNAKAN METODE MULTI TRESHOLDING DAN ALGORITMA MULTI SUPPORT VECTOR MACHINE (SVM)

IDENTIFIKASI CITRA IRIS MATA MENGGUNAKAN METODE MULTI TRESHOLDING DAN ALGORITMA MULTI SUPPORT VECTOR MACHINE (SVM)

• By Mayangky, Nissa Almira;
• Tesis
• 707
• 1-78

Identifikasi pengguna memiliki hak akses ke dalam sistem dengan mengisi nama dan kata sandi mudah untuk diprediksi, sehingga memungkinkan disalah gunakan oleh pihak ketiga, adanya latar belakang inilah yang membuat sistem biometrik digunakan. Biometrik adalah sistem identifikasi yang digunakan untuk menganalisa karakteristik fisik dan perilaku dengan teknologi optimal, Salah satu karakteristik fisiologis yang dapat dikembangkan yaitu iris. Iris merupakan organ internal yang stabil, aman dan terlindungi dengan baik, iris dari dua orang kembar identikpun berbeda, memiliki struktur fisik yang kaya dan dapat menyediakan banyak data. Pada penelitian ini digunakan metode Multi Tresholding sebagai segmentasi dan Support Vector Machine (SVM) sebagai metoda Identifikasi. Hasil yang diperoleh dari penelitian ini yaitun akurasi sebesar 93.75% dengan parameter penggunaan lima ciri pada ekstraksi ciri orde pertama yaitu Mean,
Variance, Skewness, Kurtosis, dan Entropy. Dari hasil penelitian yang telah dilakukan, sistem yang telah dibuat mampu mengidentifikasi seseorang melalui iris.

• Katakunci Biometrik, Identifikasi, Iris, Multi Tresholidng, Multi SVM
• URL http://
• Penerbit ,

REFERENSI

R. L. Krutz, R. D. Vines, and E. M. Stroz, The CISSP Prep Guide
Mastering the Ten Domains of Computer Security 1st Edition. 2001. [2] N. D. Prasetyowati, “2,2 Miliar Email dan Password Diretas.pdf,” Grid.id, 2019. https://www.grid.id/. [3] N. Hermaduanti and I. Riadi, “Automation framework for rogue access point mitigation in ieee 802.1X-based WLAN,” J. Theor. Appl. Inf.
Technol., vol. 93, no. 2, pp. 287–296, 2016. [4] M. L. Mazurek et al., “Measuring password guessability for an entire university,” Proc. ACM Conf. Comput. Commun. Secur., pp. 173–186, 2013, doi: 10.1145/2508859.2516726. [5] R. Komalasari, S. Si, and M. Kom, “Kesadaran Akan Keamanan Penggunaan Username Dan Password,” vol. 5, no. 2, pp. 56–67, 2018. [6] J. Kaye, “Self-reported password sharing strategies,” Conf. Hum. Factors
Comput. Syst. - Proc., pp. 2619–2622, 2011, doi: 10.1145/1978942.1979324. [7] R. Wash, E. Rader, R. Berman, and Z. Wellmer, “Understanding Password Choices: How Frequently Entered Passwords Are Re-used across Websites,” Twelfth Symp. Usable Priv. Secur., no. Soups, p. 175, 2016. [8] S. Tingkat, K. Kata, S. Pada, and E. D. A. N. Aplikasi, “Studi tingkat keamanan kata sandi pada data, email dan aplikasi,” no. November, 2017. [9] G. S. F. Wahid, R. Purnamasari, and S. Saidah, “Identifikasi Personal Melalui Iris Mata Dengan Menggunakan Metode Compound Local Binary Pattern Dan Klasifikasi Support Vector Machine Personal Identification Based on Compound Local Binary,” vol. 6, no. 2, pp. 3959–3966, 2019. [10] D. Akbar, “Teknologi Biometrik Zoloz denfgan Tingkat Akurasi tinggi,” 2020. https://infokomputer.grid.id/read/122065933/. [11] M. M. R, I. Wijayanto, and S. Aulia, “Biometrik Iris Recognition Menggunakan Lbp Dengan Klasifikasi Knn Biometrick Iris Recognition Using Dwt With Classifiers K-Nearest,” vol. 6, no. 1, pp. 817–825, 2019. [12] A. . Rezika, Ernawati., and A. Erlansari, “Identifikasi pola iris mata dengan 52
Program Studi Ilmu Komputer (S2) STMIK Nusa Mandiri algoritme daugman dan metode hamming distance,” vol. 6, no. 2, 2018. [13] I. G. A. A. Diatri Indradewi, “Identifikasi Iris dengan Snake Model-PSO dan Gabor 2-D,” JST (Jurnal Sains dan Teknol., vol. 7, no. 1, p. 25, 2018, doi: 10.23887/jst-undiksha.v7i1.13010. [14] D. B. Sucipto and D. Riana, “Aplikasi Diagnosa Potensi Glaukoma Melalui Citra Iris Mata Dengan Jaringan Saraf Tiruan Metode Propagasi Balik,” vol. 1, no. 3, pp. 19–27, 2013. [15] I. Pavaloi, C. D. Nita, and L. C. Lazar, “Novel matching method for automatic iris recognition using SIFT features,” ISSCS 2019 - Int. Symp.
Signals, Circuits Syst., pp. 1–4, 2019, doi: 10.1109/ISSCS.2019.8801797. [16] A. Bouaziz, A. Draa, and S. Chikhi, “Artificial bees for multilevel thresholding of iris images,” Swarm Evol. Comput., vol. 21, pp. 32–40, 2015, doi: 10.1016/j.swevo.2014.12.002. [17] K. Roy, P. Bhattacharya, and R. C. Debnath, “Multi-class SVM based iris recognition,” 2007 10th Int. Conf. Comput. Inf. Technol. ICCIT, 2007, doi: 10.1109/ICCITECHN.2007.4579426. [18] L. Lennart, “SYSTEM IDENTIFICATION,” vol. 256, no. 2001, pp. 256– 294, 2019, doi: 10.1002/047134608X.W1046.pub2. [19] D. Sugimura, T. Mikami, H. Yamashita, and T. Hamamoto, “Enhancing Color Images of Extremely Low Light Scenes Based on RGB/NIR Images Acquisition with Different Exposure Times,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 3586–3597, 2015, doi: 10.1109/TIP.2015.2448356. [20] M. Shridhar, A. S. Sethi, and M. Ahmadi, “Image Segmentation: a Comparative Study.,” Can. Electr. Eng. J., vol. 11, no. 4, pp. 172–183, 1986, doi: 10.1109/CEEJ.1986.6591942. [21] X. Munoz, J. Freixenet, X. Cufí, and J. Martí, “Strategies for image segmentation combining region and boundary information,” Pattern
Recognit. Lett., vol. 24, no. 1–3, pp. 375–392, 2003, doi: 10.1016/S0167- 8655(02)00262-3. [22] A. Pamungkas, “Multi-Level Thresholding.pdf,” 2017. https://pemrogramanmatlab.com/2017/07/26/multi-level-thresholding/. [23] O. Banimelhem and Y. A. Yahya, “Multi-thresholding image segmentation 53
Program Studi Ilmu Komputer (S2) STMIK Nusa Mandiri using genetic algorithm,” Proc. 2011 Int. Conf. Image Process. Comput.
Vision, Pattern Recognition, IPCV 2011, vol. 2, no. April 2012, pp. 1009– 1014, 2011. [24] D. Smith, “Lionbridge,” 2019. https://lionbridge.ai/articles/what-is-aitraining-data/. [25] G. M. Foody, A. Mathur, C. Sanchez-Hernandez, and D. S. Boyd, “Training set size requirements for the classification of a specific class,”
Remote Sens. Environ., vol. 104, no. 1, pp. 1–14, 2006, doi: 10.1016/j.rse.2006.03.004. [26] Rahmadya, “Training, Validating, Testing dan Corpus,” 2019. https://rahmadya.com/. [27] R. Munir, “Pengolahan Citra Digital,” Pengolah. Citra Digit., pp. 91–120, 2004. [28] A. Petrosino and G. Salvi, “Emerging Trends in Image Processing, Computer Vision and Pattern Recognition,” Emerging Trends in Image
Processing, Computer Vision and Pattern Recognition. pp. 295–314, 2015, doi: 10.1016/B978-0-12-802045-6.00019-3. [29] A. M. Reza, “Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement,” J. VLSI Signal
Process. Syst. Signal Image. Video Technol., vol. 38, no. 1, pp. 35–44, 2004, doi: 10.1023/B:VLSI.0000028532.53893.82. [30] F. Kanditami, D. Saepudin, and A. Rizal, “ANALISIS CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION ( CLAHE ) DAN REGION GROWING DALAM DETEKSI GEJALA KANKER PAYUDARA PADA CITRA MAMMOGRAM Analysis Contrast Limited Adaptive Histogram Equalization ( CLAHE ) and Region Growing To Detect The Breast Ca,” J. Elektro Itt Telkom, vol. 7 No.1, pp. 15–28, 2014. [31] N. K. Ningrum, D. Kurniawan, and N. Hendiyanto, “Penerapan Ekstraksi Ciri Orde Satu Untuk Klasifikasi Tekstur Motif Batik Pesisir Dengan Algoritma Backpropagasi,” Simetris J. Tek. Mesin, Elektro dan Ilmu
Komput., vol. 8, no. 2, p. 639, 2017, doi: 10.24176/simet.v8i2.1556. [32] Y. Permadi and . Murinto, “Aplikasi Pengolahan Citra Untuk Identifikasi 54
Program Studi Ilmu Komputer (S2) STMIK Nusa Mandiri Kematangan Mentimun Berdasarkan Tekstur Kulit Buah Menggunakan Metode Ekstraksi Ciri Statistik,” J. Inform., vol. 9, no. 1, 2015, doi: 10.26555/jifo.v9i1.a2044. [33] Vladimir.Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995. [34] V. Cherkassky and F. Mulier, Learning from Data - Concepts, Theory and
Methods, vol. 32, no. 5. New Yor, 1998. [35] B. Clarke, E. Fokoue, and H. H. Zhang, Principles And Theory For Data
Mining And Machine Learning. New York, USA.: Springer Science + Bussiness Media, 2009. [36] C. Campbell and Y. Ying, Learning with Support Vector Machines edition
Synthesis Lectures on Artificial Intelligence and Machine Learning. UK: Morgan & Claypool Publisher, 2011. [37] B. Schlkopf and A. J. Smola, Learning with Kernels Support Vector
Machines, Regularization, Optimization, and Beyond Adaptive
Computation and Machine Learning 1st Edition. USA: MIT Press, Massachusetts Institute of Technology, 2001. [38] Hamel. H Lutz, Knowledge Discovery with Support Vector Machines. New Jersey, USA: John Wiley & Sons, Inc, 2009. [39] A. Shrivastava, J. K. Pillai, and V. M. Patel, “Multiple kernel-based dictionary learning for weakly supervised classification,” Pattern
Recognit., vol. 48, no. 8, pp. 2667–2675, 2015, doi: 10.1016/j.patcog.2015.03.005. [40] F. Gorunescu, Data Mining Concepts, Models and Techniques. Verlag Berlin Heidelberg: Springer, 2011. [41] M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009, doi: 10.1016/j.ipm.2009.03.002. [42] M. Dobeš, L. Machala, P. Tichavský, and J. Pospíšil, “Human eye iris recognition using the mutual information,” Optik (Stuttg)., vol. 115, no. 9, pp. 399–404, 2004, doi: 10.1078/0030-4026-00388. [43] A. Eleyan and B. Karlik, “Sift-based Iris Recognition Using Sub- 55
Program Studi Ilmu Komputer (S2) STMIK Nusa Mandiri Segments,” pp. 350–353, 2013. [44] S. Thepade and P. R. Mandal, “Energy compaction based novel Iris recognition techniques using partial energies of transformed iris images with Cosine, Walsh, Haar, Kekre, Hartley Transforms and their Wavelet Transforms,” 11th IEEE India Conf. Emerg. Trends Innov. Technol.
INDICON 2014, 2015, doi: 10.1109/INDICON.2014.7030641. [45] S. D. Thepade and R. K. Bhondave, “Multimodal identification technique using Iris & Palmprint traits with matching score level in various Color Spaces with BTC of bit plane slices,” 2015 Int. Conf. Ind. Instrum. Control.
ICIC 2015, vol. 00, no. c, pp. 1469–1473, 2015, doi: 10.1109/IIC.2015.7150981. [46] M. S. Madane and S. D. Thepade, “Score Level Fusion based Multimodal Biometric Identification Using Thepade’s Sorted Ternary Block Truncation Coding With Variod Proportion of Iris, Palmprint,Left fingerprint & Right fingerprint With Asorted Similarity Measures & Different Colorspaces,” pp. 824–828, 2016. [47] A. Herliana and T. Arifin, “Analisis Tekstur Pada Citra Iris Mata Menggunakan Algoritma Gray Level Co-Occurency Matrix,” J. Pilar Nusa
Mandiri, vol. 15, no. 2, pp. 183–188, 2019, doi: 10.33480/pilar.v15i2.680. [48] C. Narbuko and H. A. Achmadi, Metodologi Penelitian, no. January. Jakarta: PT Bumi Aksara, 2007.