Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara
Abstract
Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar 98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %.Kata kunci: kanker payudara, neural network, support vector machine, naive bayes
Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate.
Keywords: breast cancer, neural network, support vector machine, naive bayes
References
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[3] Gharibdousti, M. S., Haider, S. M., Ouedraogo, D., & Lu, S. (2019). Breast cancer diagnosis using feature extraction techniques with supervised and unsupervised classification algorithms. Applied Medical Informatics Original Research, 41(1), 40–52.
[4] Higa, A. (2018). Diagnosis of Breast Cancer using Decision Tree and Artificial Neural Network Algorithms. International Journal of Computer Applications Technology and Research, 7(1), 23–27. https://doi.org/10.7753/ijcatr0701.1004
[5] International Agency for Research on Cancer, “Indonesia - Global Cancer Observatory†[Online]. Available: https://gco.iarc.fr/today/data/ factsheets/populations/360-indonesia-fact-sheets.pdf. [Accessed: 17-Maret-2020].
[6] Ibeni, W. N. L. W. H., Salikon, M. Z. M., Mustapha, A., Daud, S. A., & Salleh, M. N. M. (2019). Comparative analysis on bayesian classification for breast cancer problem. Bulletin of Electrical Engineering and Informatics, 8(4), 1303–1311. https://doi.org/10.11591/eei.v8i4.1628
[7] Liao. Recent Advances in Data Mining of Enterprise Data: Algorithms and Application . Singapore: World Scientific Publishing. 2007.
[8] Lorena, S., Ginting, B. R., & Permana, A. A. (2016). Penerapan Data Mining Untuk Klasifikasi Kelayakan Nasabah Dalam Pengajuan Kredit Menggunakan. 1–10.
[9] Prasetyo, Eko. (2012) “Data Mining Konsep dan Aplikasi menggunakan MATLABâ€, Andi,Yogyakarta.
[10] Alamsyah dkk (2017). Implementation Of Naive Bayes Method In Classification Of Breast Cancer Disease. 2(1), 191–194.
Published
2020-06-28
How to Cite
DERISMA, Derisma; FEBRIAN, Fajri.
Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara.
BINA INSANI ICT JOURNAL, [S.l.], v. 7, n. 1, p. 53-62, june 2020.
ISSN 2527-9777.
Available at: <https://87383.shichuantrade-tw.tech/index.php/BIICT/article/view/1343>. Date accessed: 01 dec. 2024.
doi: https://doi.org/10.51211/biict.v7i1.1343.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
