47
DAFTAR PUSTAKA
Andari, S., 2012, Smooth Support Vector Machine dan Multivariate Adaptive Regression Splines Untuk Mendiagnosis Kanker Payudara, Tesis, Mahasiswa Jurusan Statistika Fakultas MIPA ITS, Surabaya.
Astuti, Puji. & Garnadi, Agah. 2009. On Eigenvalue and Eigenvectot of Perturbed Pairwise Comparison Matrices, ITB J.Sci 41(2): 69-77
Bandyopadhyay, S.K. 2010. pre-processing of Mammogram Images, International Journal of Engineering Science andTechnology 2(11) : 6753-6758.
Furqan,M.,Embong, A.(2009). Smooth Support Vector Machine For Face Recognition Using Principal Component Analysis, International Journal of Science Engineering and Technology 2(3): 1985-3785.
Ferlay, J., Shin, H.R., Bray, F., Forman, D., Mathers, C., & Parkin, D.M. 2008. GLOBOCAN 2008 v1.2, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 10 [online]. Lyon, France: International Agency for Research on Cancer 2010 2(3): 85-97..
Gunn, S. 1998. Support Vector Machines for Clasification and Regression, Technical Report, ISIS 4(3): 1985-3785..
Gatos, B., Kesidis,A.L. & Papandreou, A. 2011. Adaptive zoning features for character and word recognition. Di dalam: 11th International Conference on Document Analysis and Recognition; Beijing, 18-21 Sep 2011. Washington DC: IEEE Computer Society 3: 1160-1164.
http://abacus.ee.cityu.edu.hk/imagedb/cgi-bin/ ibrowser/ibrowser.cgi? folder=/ Medical _Image/mammogram/.27 Oktober 2014.
Habibi, R. 2011. Penerapan Teknik Data Mining dengan Metode Smooth Support Vector Machine (SSVM) untuk memprediksi mahasiswa yang berpeluang drop out (studi kasus mahasiswa politeknik negeri medan). Tesis. Universitas Sumatera Utara.
Han, Jiawei. & Kamber, Micheline. 2000. Data Mining : Concepts and Techniques, Morgan Kaufmann Publishers, Urbana-Champaign
Hegadi, R. S. 2012. Recognition of Printed Kannada Numerals based onZoning Method,
International Journal of Computer Applications on National Conference on Advanced Computing and Communications – NCACC 1: 0975 – 8878.
48
Huang, C.M., Lee, Y.J., Lin, D.K.J. & Huang, S.Y. (2007), Model selection for support vector machine via uniform design, Computational Statistics and Data Analysis 52: 335-346.
Indrati, S. & Madenda, S. 2010. Ekstraksi Fitur Bentuk Tumor Payudara. Depok: Universitas Gunadarma.
Kartar, S., Renu, D. & Rajneesh, R. 2011. Handwritten Gurumukhi Character Recognition Using Zoning Density and Background Directional Distribution Features. International Journal of Computer Science and Information Technologies, 2 (3) :1036-1041
Karnea, A.S. & Navalgunda, S.S. 2013. Implementation of an Image Thinning Algorithm using Verilog and MATLAB, International Journal of Current Engineering and Technology 1(1) : 2277 – 4106.
Kamavisdar, P., Saluja, S. & Agrawal, S. 2013. A Survey on Image Classification Approaches and Techniques, International Journal of Advanced Research in Computer and Communication Engineering 2(1): 2278-1021
Lee, Y.J. & Mangasarian, O.L. 2001. A smooth support vector machine, Jurnal Computational Optimization and Application 20: 5-22.
Luo, L., Lin, C., Peng, H. & Zhou, Q. 2006. A Study on Piecewise Polynomial Smooth Approximation to the Plus Function,In proceedings of the ICARCV 1: 1- 6.
Mangasarian, O.L., & Musicant, D.R. 1999. Succesive overrelaxation for support vector machines, IEEE Transactions on Neural Network, 10: 1032 – 1037.
Malagelada, A.O.I. 2007. Automatic Mass Segmentation in Mammographics Image, in Departement of Electronics Comp. Disertasi Computer Science and Automatic Control. Universitat de Girona.
Nithya, R., & Santhi, B. 2011. Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer. International Journal of Computer Applications 28(6): 0975 – 8887.
Putra, D. 2009. Sistem Biometrika Konsep Dasar, Teknik Analisis Citra, dan Tahapan Membangun Aplikasi Sistem Biometrika. Yogyakarta: Andi.
Purwitasari, D. 2011. Implementasi Adaptive Support Vector Machine untuk Membantu Identifikasi Kanker Payudara. Surabaya: Kampus ITS.
Purnami, S.W., Embong, A., Zain, J.M., & Rahayu, S.P. 2009. A Comparison of Smoothing Function In Smooth Support Vector Machine, will be presented in International Conference on Software Engineering & Computer Systems.
49
Purnami, S.W., Embong, A., Zain, J.M., & Rahayu, S.P. 2009. A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis, Journal of Computer Science 5(12): 1003-1008.
Sundaram,M., Sasikala, D. & Rani, P.A. 2014. A Study On Preprocessing A Mammogram Image Using Adaptive Median Filter, International Journal of Innovative Research in Science, Engineering and Technology 3(3) : 2319-8753.
Vapnik, V. 1998. Statistical Learning Theory, Wiley New York
Wanpeng, C., Rensheng.C., & Dong.Y. 2008. ’’ An illumination-independent edge detection and fuzzy enhancement algorithm based on wavelet transform for nonuniform weak illumination images,” 29: 192–199.
Yuan, Y., & Huang, T. 2005. A Polynomial Smooth Support Vector Machine for Classification, Springer-Verlag, Berlin Heidelberg, 3584: 157-164.
Yuan, Y., Yan J., & Xu, C. 2005, Polynomial Smooth Support Vector Machine (PSSVM), Chinese Journal of Computers, 28: 9-17.
Yuan, Y., Fan, W., & Pu, D. 2007. Spline Function Smooth Support Vector Machine For Clasification, Journal of Industrial and Management Optimization 3(3): 529 – 542.
