Teknik Data Mining untuk Prediksi Kanker Payudara yang Efisien

Article Sidebar

PDF
Published: Sep 29, 2021
DOI: https://doi.org/10.52005/fidelity.v3i3.100
(Abstract view) : 291
(PDF download) : 477
Keywords:
kanker payudara, klasifikasi, Pohon keputusan, Naïve Bayes, MLP, Regresi Logistik SVM, KNN dan weka

Main Article Content

Irfan Solikin
R&D PT. Bhumi Jati Power Jakarta

Abstract

Salah satu keganasan paling umum pada wanita, kanker payudara, juga merupakan salah satu penyebab utama kematian. Menurut Organisasi Kesehatan Dunia, kanker payudara sekarang menjadi keganasan paling umum di antara wanita di seluruh dunia. Untuk menyelamatkan nyawa, identifikasi dini kanker payudara sangat penting. Akurasi klasifikasi dari database Wisconsin Breast Cancer (WBC) digunakan untuk membandingkan berbagai pengklasifikasi Data Mining dalam penelitian ini. Bertujuan untuk akurasi prediksi yang tinggi, pekerjaan ini bermaksud untuk mengembangkan model klasifikasi akurat untuk prediksi kanker payudara yang sepenuhnya menggunakan informasi berharga yang ditemukan dalam data klinis. Berdasarkan data WBC, kami telah menjalankan uji coba. Ini dibagi menjadi dua set: set latihan 499 pasien dan set tes dunia nyata 200. Menggunakan perangkat lunak Weka, eksperimen ini menganalisis enam strategi kategorisasi dan menemukan bahwa Support Vector Machine (SVM) lebih akurat dalam memprediksi masa depan daripada teknik lain yang diuji. Keakuratan beberapa teknologi deteksi kanker payudara sedang diselidiki dan dibandingkan. SVM lebih cocok untuk menangani kesulitan klasifikasi seperti prediksi kanker payudara. Jadi kami menyarankan untuk menerapkan temuan ini pada masalah klasifikasi lainnya juga

Article Details

How to Cite
[1]
I. Solikin, “Teknik Data Mining untuk Prediksi Kanker Payudara yang Efisien”, Fidelity, vol. 3, no. 3, pp. 63-67, Sep. 2021.
Issue
Vol 3 No 3 (2021): Edition for September 2021
Section
Articles

Lisensi Creative Commons
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with FIDELITY : Jurnal Teknik Elektro agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. 
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Lisensi Creative Commons
Jurnal FIDELITY : Jurnal Teknik Elektro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Received 2021-06-05
Accepted 2021-08-05
Published 2021-09-29

References

American Cancer Society. Breast Cancer Facts & Figures 2005-2006. Atlanta: American Cancer Society, Inc. (http://www.cancer.org/).

A.Bellachia and E.Guvan,"Predicting breast cancer survivability using data mining techniques", Scientific Data Mining Workshop, in conjunction with the 2006 SIAM Conference on Data Mining, 2006.

A. Endo, T. Shibata and H. Tanaka (2008), Comparison of seven algorithms to predict breast cancer survival, Biomedical Soft Computingand Human Sciences, vol.13, pp.11-16.

Breast Cancer Wisconsin Data [online]. Available: http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data.

Brenner, H., Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. Lancet. 360:1131–1135, 2002.

D. Delen, G. Walker and A. Kadam (2005), Predicting breast cancer survivability: a comparison of three data mining methods, Artificial Intelligence in Medicine, vol.34, pp.113-127.

Ian H. Witten and Eibe Frank. Data Mining: Practical machine learning tools and techniques, 2nd Edition. San Fransisco:Morgan Kaufmann; 2005.

J. Han and M. Kamber, Data Mining—Concepts and Technique (The Morgan Kaufmann Series in Data Management Systems), 2nd ed. San Mateo, CA: Morgan Kaufmann, 2006.

J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA:Morgan Kaufmann; 1993.

Mitchell, T. M., Machine Learning, McGraw-Hill Science/Engineering/Math, 1997

P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Reading, MA: Addison-Wesley, 2005.

Razavi, A. R., Gill, H., Ahlfeldt, H., and Shahsavar, N., Predicting metastasis in breast cancer: comparing a decision tree with domain experts. J. Med. Syst. 31:263–273, 2007.

S.B.Kotsiantis and P.E.Pintelas,"Combining Bagging and Boosting", International Journal of Information and Mathematical Sciences, 1:4 2005.

Vapnik, V. N., The nature of statistical learning theory. Springer, Berlin, 1995.

Weka: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/

Witten H.I., Frank E., Data Mining: Practical Machine Learning Tools and Techniques, Second edition, Morgan Kaufmann Publishers, 2005.