Aplikasi Diagnosis Penyakit Kanker Payudara Menggunakan Algoritma Sequential Minimal Optimization

Application of Breast Cancer Diagnosis Using Sequential Minimal Optimization Algorithm

*Agung Wibowo -  Department of Informatics, STMIK Nusa Mandiri Sukabumi, Indonesia
Received: 29 Aug 2017; Published: 29 Oct 2017.
DOI: https://doi.org/10.14710/jtsiskom.5.4.2017.153-158 Download
Breast_diagnosis model manual
Subject model calculation manual
Type Data Analysis
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Subject hasil uji model
Type Research Materials
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Souce code
Subject source code aplikasi
Type Source Text
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Open Access Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer
License URL: http://creativecommons.org/licenses/by-sa/4.0

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Section: Articles
Language: ID
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Various methods for the diagnosis of breast cancer exist, but not many have been implemented as an application. This study aims to develop an application using SMO algorithm assisted by Weka to diagnose breast cancer. The application was web-based application and developed using Javascript. Test dataset and model formation used original Breast Cancer Database (WBCD) data without missing value. Test mode used 10-fold cross-validation. This application can diagnose breast cancer with an accuracy of 97.3645% and has a significant increase in accuracy for the diagnosis of malignant cancer.

Note: This article has supplementary file(s).

Breast cancer detection; cancer diagnostic algorithm; Weka; SMO accuracy

Article Metrics:

  1. A. O. Bilska-Wolak, C. E. Floyd Jr., L. W. Nolte, and J. Y. Lo, "Application of Likelihood Ratio to Classification of Mammographic Masses Performance Comparison to Case-based Reasoning," Medical Physics, vol. 10, no. 5, pp. 949-958, Mei 2003.
  2. A. F. Rahmah and E. L. Widuri, "Post Traumatic Growth pada Penderita Kanker Payudara," Humanitas, vol. 8, no. 2, pp. 114-128, Agustus 2011.
  3. E. S. Wahyuni, "Penerapan Metode Seleksi Fitur untuk Meningkatkan Hasil Diagnosis Kanker Payudara," Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 7, no. 1, pp. 283-294, April 2016.
  4. S. Gupta, P. F. Chyn, and M. K. Markey, "Breast Cancer CAD x Based on BI-RADS™ Descriptors from Two Mammographic Views," Medical Physics, vol. 33, no. 6, pp. 1810- 1817, Juni 2006.
  5. M. Elter, R. Schulz-Wendtland, and T. Wittenberg, "The Prediction of Breast Cancer Biopsy Outcomes using Two CAD Approaches that Both Emphasize an Intelligible Decision Process," Medical Physics, vol. 34, no. 11, pp. 4164-4172, November 2007.
  6. H. Cho et al., "A Similarity Study of Content-based Image Retrieval System for Breast Cancer using Decision Tree," Medical Physics, vol. 40, no. 1, pp. 012901-1 - 012901-13, January 2013.
  7. R. Primartha and N. Fathiyah, "Sistem Pakar Fuzzy Untuk Diagnosis Kanker Payudara Menggunakan Metode Mamdani," Generic, vol. 8, no. 1, pp. 190-197, Maret 2013.
  8. M. Mutlimah, "Aplikasi Sistem Fuzzy untuk Diagnosa Kanker Payudara (Breast Cancer)," Universitas Negeri Yogyakarta, Yogyakarta, Desertasi 2014.
  9. G. P. Nabilah and S. Kusumadewi, "Fuzzy Inference System untuk Penentuan Resiko Kanker Payudara," in SNATI, Kudus, 2015, pp. 101-109.
  10. N. U. Makhfudhoh, "Klasifikasi Kanker Payudara dari Citra Mammografi Menggunakan Model Fuzzy Neural Network," Disertasi, Universitas Negeri Yogyakarta, Yogyakarta, 2014.
  11. F. A. Novianti and S. W. Purnami, "Analisis Diagnosis Pasien Kanker Payudara Menggunakan Regresi Logistik dan Support Vector Machine (SVM) Berdasarkan Hasil Mamografi Fourina Ayu Novianti dan Santi Wulan Purnami," Jurnal Sains dan Seni, vol. 1, no. 2, pp. D-147 - D-152, September 2012.
  12. N. K. Dewa, "Rancang Bangun Ssistem Pakar Diagnosis Penyakit Kanker Payudara Menggunakan DEMPSTER – SHAFER," Disertasi, Universitas Pesantren Tinggi Darul Ulum, Jombang, 2016.
  13. A. M. Zamani, B. Amaliah, and A. Munif, "Implementasi Algoritma Genetika pada Struktur Backpropagation Neural Network untuk Klasifikasi Kanker Payudara," Jurnal Teknik ITS, vol. 1, no. 1, pp. A-222 - A-227, September 2013.
  14. M. Triaji, "Sistem Pakar Pendeteksi Kanker Payudara Menggunakan Metode Certainty Factor," Skripsi, Universitas Dian Nuswantoro, Semarang, 2012.
  15. I. Zulkarnaini, "Diagnosa Penyakit Kanker Payudara dengan Menggunakan Sistem Fuzzy Multi Attribute Decision Making," LENTERA, vol. 13, no. 2, pp. 53-56, Juni 2013.
  16. P. A. Maulida, "Decision Tree dengan Algoritma ID3 untuk Melakukan Deteksi," Skripsi, Universitas Dian Nuswantoro, Semarang, 2015.
  17. Y. V. Via, B. Nugroho, and A. Syafrizal, "Sistem Pendukung Keputusan Klasifikasi Tingkat Keganasan Kanker Payudara dengan Metode Naive Bayes Classifier," SCAN, vol. 10, no. 2, pp. 63-68, Januari 2015.
  18. A. R. Safutra and D. W. Prabowo, "Diagnosis Penyakit Kanker Payudara Menggunakan Metode Naive Bayes Berbasis Desktop," Jurnal Penelitian Dosen FIKOM (UNDA), vol. 6, no. 1, pp. 1-6, Oktober 2016.
  19. Center for MachineLearning and Intelligent Systems, "UCI Machine Learning Repository," [Onlne]. Available: https://archive.ics.uci.edu/ml/datasets.htm. [Diakses: Okt. 26, 2017].
  20. A. Urmaliya and J. Singhai, "Sequential Minimal Optimization for Support Vector Machine with Feature Selection in Breast Cancer Diagnosis," in Proc. 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), Shamla, India, 2013, pp. 481-486.
  21. J. C. Platt, "Sequential Minimal Optimization:A Fast Algorithm for Training Support Vector Machines," Microsoft Research, Technical Report MSR-TR-98-14, 1998.
  22. O. L. Mangasarian and W. H Wolberg, "Cancer Diagnosis via Linear Programming," SIAM News, vol. 23, no. 5, pp. 1-18, September 1990.
  23. WEKA. (2010, Maret) SourceForge. [Online]. http://weka.sourceforge.net/doc.stable/weka/filters/unsupervised/attribute/Normalize.html
  24. Hitachi Group Company. (2008, Desember) Pentaho. [Online]. http://wiki.pentaho.com/display/DATAMINING/Normalize+%28attribute%29
  25. J. Han, M. Kamber and J. Pei, Data Mining Concepts and Techniques, 3rd ed., Morgan Kauffman, Ed. Waltham, Massachusetts: Elsevier Inc, 2011.