Evaluasi Tiga Jenis Algoritme Berbasis Pembelajaran Mesin untuk Klasifikasi Jenis Tumor Payudara

Annisa Handayani, Ade Jamal, Ali Akbar Septiandri


According to the World Health Organization (WHO), malignant tumor (cancer) is one of the leading causes of mortality worldwide. Among all types of malignant tumors, malignant breast tumor (breast cancer) is the most common malignant tumors found, especially in women. One of four ways to distinguish benign breast tumor from malignant ones is by doing Fine Needle Aspiration (FNA) test. This method is simple, quick, inexpensive, and can be performed either in outpatients or inpatients. Although FNA is much more preferred than the other methods, the accuracy of FNA varies widely. Therefore, this research is conducted to find the best model to classify malignant breast tumors and benign breast tumors, based on data from FNA test by evaluating value of Area Under the Curve (AUC) of three algorithms, Extreme Gradient Boosting (XGBoost), Support Vector Machine with Radial Basic Function kernel (SVM-RBF), and Multi-layer Perceptron (MLP). The breast cancer data set used in this research is obtained from Wisconsin Breast Cancer (Original) Data set available in the UCI Machine Learning Repository. The experiment shows that SVM with eliminating missing value data set achieved the best result, with AUC value 99.23 and cost $2,740.20.


Klasifikasi, Tumor Payudara, Wisconsin Breast Cancer (WBC), Extreme Gradient Boosting (XGBoost), Support Vector Machine Kernel Radial Basic Function (SVM-RBF), Multilayer Perceptron (MLP)

Full Text:



Yayasan Kanker Indonesia. (2016) Yayasan Kanker Indonesia. [Online]. http://yayasankankerindonesia.org/tentang-kanker/

World Health Organization. (2008) Global Burden of Disease. [Online]. http://www.who.int

American Cancer Society, Global Cancer Facts and Figures, 3rd ed. Atlanta: American Cancer Society, 2015. [Online]. https://www.cancer.org

Kementerian Kesehatan RI. (2015) Pusat Data dan Informasi Kementerian Kesehatan RI. [Online]. http://www.depkes.go.id

G. J. Miao, K. H. Miao, and J. H. Miao, "Neural Pattern Recognition Model for Breast Cancer Diagnosis," Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Bioinformatics (JBIO), pp. 1-8, August 2012.

S. W. Fletcher, W. Black, R. Harris, B. K. Rimer, and S. Saphiro, "Report of The International Workshop on Screening Breast Cancer," Journal of National Cancer Institute, vol. 85, pp. 1644-1656, 1993.

H. Al-Khawari, R. Athyal, A. Kovacs, M. Al-Saleh, and J. P. Madda, "Accuracy of the Fischer Scoring System and Breast Imaging Reporting and Data System Identification of Malignant Breast Lesions," Annals of Saudi Medicine, vol. 29, no. 4, pp. 280-287, August 2009.

W. L. Street, W. N. Street, and W. H. Wolberg, "Breast Cancer Diagnosis and Prognosis via Linear Programming," Mathematical Programming Technical Report, pp. 1-10, December 1994.

Z. A. Prasetyo, "Uji Diagnostik FNAB (Fine Needle Aspiration Biopsy) dibandingkan dengan Biospy Patologi Anatomi dalam Mendiagnosis Karsinoma Tiroid," Universitas Diponegoro, Semarang, Karya Tulis Ilmiah 2012. [Online]. epints.undip.ac.id

C. P. Utomo, A. Kardiana, and R. Yuliwulandari, "Breast Cancer Diagnosis using Artificial Neural Networks with Extreme learning Techniques," International Journal of Advanced Research in Artificial Intelligence, 2014.

I. Muhic, "Fuzzy Analysis of Breast Cancer Disease Using Fuzzy CMeans adn Pattern recognition," Southeast Europe Journal of Soft Computing, pp. 50-55, 2013.

T. Kiyan and T. Yildrim, "Breast Cancer Diagnosis Using Statistical Neural Networks," Journal of Electrical & Electronics Engineering, p. Vol.4, 2004.

A. Aloraini, H. Mousanni, H. A. Moatassime, and N. Thomas, "Different Machine Learning Algorithms for Breast Cancer Diagnosis," International Journal of Artificial Intelligence & Applications (IJAIA), 2012.

K. Sivakami and Nadar Saraswathi, "Mining Big Data: Breast Cancer Prediction using DT - SVM Hybrid Model," International Journal of Scientific Engineering and Applied Science (IJSEAS), p. Vol.1, 2015.

K. Menaka and S. Karpagavalli, "Breast Cancer Classification using Support Vector Machine and Genetic Programming," International Journal of Innovative Research in Computer and Communication Engineering, p. Vol.1, 2013.

M. A. B. Ahmad, "Mining Health Data for Breast Cancer Diagnosis Using Machine Learning," University of Canberra, Canberra, PhD Thesis December 2013. [Online]. http://www.canberra.edu.au

V. Kumar, R. S. Cotran, and S. L. Robbins, Buku Ajar Patologi, 7th ed. Jakarta, Indonesia: EGC, 2007.

D. K. Roy and L. K. Sharma, "Genetic k-Means Clustering Algorithm for Mixed Numeric and Categorical Data Sets," International Journal of Artificial Intelligence & Applications, vol. 1, no. 2, pp. 23-38, 2010.

J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. San Fransisco, United States of America: Morgan Kaufmann, 2006.

N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. United Kingdom: Cambridge University Press, 2000.

M. J. Zaki and W. M. Jr., Data Mining and Analysis Fundamental Concepts and Algorithms. New York, USA: Cambridge University Press, 2014.

C. W. Hsu, C. C. Chang, and C. J. Lin, A Practical Guide to Support Vector Classification. Taipei, Taiwan: National Taiwan University, 2003.

C. M. Bishop, Pattern Recognition and Machine Learning. New York, USA: Springer, 2006.

L. Fausett, Fundamental of Neural Networks: Architectures, Algorithms, and Applications. New York, USA: Prentice-Hall, 1994.

X. Jin et al., "Deep Learning with S-Shaped Rectified Linear Activation Units," CoRR, vol. abs/1512.07030, December 2015.

S. N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to Neural Networks using Matlab 6.0. New Delhi, India: Tata Mcgraw-Hill, December 2006.

J. H. Friedman, "Greedy Function Approximation: A Gradient Boosting," Annals of Statistics, vol. 38, no. 4, pp. 367-378, 2001.

T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in KDD'16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery Data Mining, California, March 2016, pp. 785-794.

F. Gorunescu, Data Mining Concepts, Models and Techniques, 12th ed. Berlin, Germany: Springer, 2011.

O. L. Mangasarian, "Cancer Diagnosis via Linear Programming," SIAM News, vol. 23, no. 5, pp. 1-18, 1990.

K. Hukkinen, L. Kivisaari, P. S. Heikkila, K. V. Smitten, and M. Leidenius, "Unsuccessful Preoperative Biopsies, Fine Needle

Asspiration Cytology or Core Needle Biopsy, Leade to Increased Costs in The Diagnostic Workup in Breast Cancer," Acta Oncologica, vol. 477, no. 6, pp. 1037-1045, 2008.

K. R. Yabroff, C. J. Bradley, A. B. Mariotto, M. L. Brown, and E. J. Feuer, "Estimates and Projections of Value of Life Lost From Cancer Death in the United States," Journal of the National Cancer Institute (JNCI), vol. 100, no. 24, pp. 1755-1762, 2008.

M. J. Yaffe, R. Jong, E. D. Pisano, K. I. Pritchard, and R. A. Smith, "Earlier Detection and Diagnosis of Breast Cancer," Canadian Breast Cancer Foundation Ontario, Toronto, Recommendation and Scientific Review from It's About Time! A Consensus Conference.

DOI: http://dx.doi.org/10.22146/jnteti.v6i4.350


  • There are currently no refbacks.

Copyright (c) 2017 Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik Universitas Gadjah Mada
Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
+62 274 552305