Klasifikasi Sel Tumor Payudara Menggunakan Algoritma Support Vector Machine (SVM)

  • Puspa Rismawati Universitas Mulawarman
  • Adrianus Inu Natalisanto Program Studi Fisika, Jurusan Fisika, FMIPA Universitas Mulawarman
  • Devina Rayzy Perwitasari Sutaji Putri Program Studi Fisika, Jurusan Fisika, FMIPA Universitas Mulawarman

Abstract

Cancer is a large group of diseases that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably, beyond their normal limits to invade adjacent parts of the body and/or spread to other organs. There is a lot of information about breast cancer that can be accessed easily. Information about breast cancer can be processed with machine learning. Machine learning can discover new meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technology and statistical and mathematical techniques. The purpose of this research is to determine the value of the accuracy of the SVM model on training data and testing data; and to determine the precision value of the SVM model on training data and testing data. Wisconsin Breast Cancer (WBC) data available in the UCI Machine Learning Repository. The data have been processed using the Python programming language with a support vector machine (SVM) modeling algorithm. The results of this research indicate that the value of accuracy in training data was equal to , the value of accuracy in testing data was equal to , and the value of precision in the SVM model algorithm was obtained as large as  for training data and as large as  for data testing.

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References

[1] Chazar, C., & Erawan, B. (2020). Machine Learning Diagnosis Kanker Payudara Menggunakan Algoritma Support Vector Machine. INFORMASI (Jurnal Informatika Dan Sistem Informasi), 12(1), 67-80.
[2] Hammad, R., Kurniasih, J., Hasan, N. F., Dengen, C. N., & Kusrini, K. (2019). Prototipe Machine Learning Untuk Prognosis Penyakit Demensia (The Prototype of Machine Learning for The Prognosis of Dementia). JURNAL IPTEKKOM (Jurnal Ilmu Pengetahuan & Teknologi Informasi), 21(1), 17- 29.
[3] Handayani, A., Jamal, A., & Septiandri, A. A. (2018). Evaluasi Tiga Jenis Algoritme Berbasis Pembelajaran Mesin untuk Klasifikasi Jenis Tumor Payudara. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI). https://doi.org/10.22146/jnteti.v6i4.350
[4] Indrati, A., & Madenda, S. (2009). Ekstraksi Fitur Bentuk Tumor Payudara. In Seminar Nasional Aplikasi Teknologi Informasi (SNATI).
[5] Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. Elsevier. https://doi.org/10.1016/j.csbj.2014.11.005
[6] Mahendra, G. S., & Ernanda Aryanto, K. Y. (2019). SPK Penentuan Lokasi ATM Menggunakan Metode AHP dan SAW. Jurnal Nasional Teknologi Dan Sistem Informasi, 5(1), 49–56. https://doi.org/10.25077/teknosi.v5i1.2019.49-56
[7] Maulana, M. S., Sabarudin, R., & Nugraha, W. (2019). Prediksi Ketepatan Kelulusan Mahasiswa Diploma dengan Komparasi Algoritma Klasifikasi. JUSTIN (Jurnal Sistem dan Teknologi Informasi), 7(3), 202-206.
[8] Nugraha, F. S., Shidiq, M. J., & Rahayu, S. (2019). ANALISIS ALGORITMA KLASIFIKASI NEURAL NETWORK UNTUK DIAGNOSIS PENYAKIT KANKER PAYUDARA. Jurnal Pilar Nusa Mandiri, 15(2), 149–156. https://doi.org/10.33480/pilar.v15i2.601
[9] Seyam, M., Othman, F., & El-Shafie, A. (2017). Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines. In MATEC Web of Conferences (Vol. 111). EDP Sciences. https://doi.org/10.1051/matecconf/201711101007
[10] Umadevi, T. P., & Murugan, A. (2021). Enhanced handwritten document recognition using confusion matrix analysis. Advances in Parallel Computing, 39, 121– 126. https://doi.org/10.3233/APC210131
Published
2024-06-27
How to Cite
RISMAWATI, Puspa; NATALISANTO, Adrianus Inu; SUTAJI PUTRI, Devina Rayzy Perwitasari. Klasifikasi Sel Tumor Payudara Menggunakan Algoritma Support Vector Machine (SVM). Progressive Physics Journal, [S.l.], v. 5, n. 1, p. 356-361, june 2024. ISSN 2722-7707. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/ppj/article/view/1092>. Date accessed: 16 oct. 2024. doi: https://doi.org/10.30872/ppj.v5i1.1092.

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