Klasifikasi Citra Mammogram Dengan Metode Ekstraksi Ciri Zoning Menggunakan Ssvm
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 and Technology 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.
Universitas Sumatera Utara
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.
Universitas Sumatera Utara
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.
Universitas Sumatera Utara
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 and Technology 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.
Universitas Sumatera Utara
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.
Universitas Sumatera Utara
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.
Universitas Sumatera Utara