PREDIKSI KEKAMBUHAN KANKER PAYUDARA DENGAN ALGORITMA C4.5
Keywords:
Cancer, Breast Cancer, Data Mining, Classification, Naïve Bayes, C 4.5 Algorithem
Abstract
Breast cancer is known as the fifth cause of death based on WHO data in 2015. The risk of developing breast cancer will increase with age, family medical history, personal medical history, caucasian descent, early menstruation, late menopause and others. This study aims to predict the use of Naïve Bayes and C4.5 data mining algorithms to classify the recurrence of cancer patients based on certain attributes in the breast cancer dataset. The data mining process will help identify the range or value of various attributes of what causes breast cancer. The results of this study indicate that the C4.5 algorithm has an accuracy value of 75.5% better than Naïve Bayes which only has an accuracy value of 72.7%.
References
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Harris, J. R., Lippman, M. E., Morrow, M., & Osborne, C. K. (2014). Evaluation After Primary Therapy and Management of Reccurent Breast Cancer. In Diseases of the breast: Fifth edition (1st ed., p. 871). London: Wolters Kluwer Health Adis (ESP).
Kristanto, A. D., & Kahija, Y. F. La. (2017). PENGALAMAN COPING TERHADAP DIAGNOSIS KANKER PADA PENDERITA USIA KERJA DI RUMAH SAKIT MARGONO SOEKARJO PURWOKERTO. Jurnal Empati, 6(2), 1–9. Retrieved from https://ejournal3.undip.ac.id/index.php/empati/article/viewFile/19723/18656
Pritom, A. I., Munshi, M. A. R., Sabab, S. A., & Shihab, S. (2016). Predicting Breast Cancer Recurrence Uusing Effective Classification and Feature Selection Technique. In 2016 19th International Conference on Computer and Information Technology (ICCIT) (pp. 310–314). Dhaka: IEEE. https://doi.org/10.1109/ICCITECHN.2016.7860215
Rizqiah, A. R., & Subekti, A. (2018). PREDIKSI KEKAMBUHAN KANKER PAYUDARA DENGAN ALGORITMA C4.5. Jakarta.
WHO (2018, February 01). Cancer. July 20, 2018. http://www.who.int/news-room/fact-sheets/detail/cancer
Eveline, K., Purwanto, H., & Lestari, P. (2017). Faktor Klinis dan Histopatologi serta Hubungannya dengan Kekambuhan Pascaoperasi pada Pasien Kanker Payudara di RSUD Dr. Soetomo, Januari–Juni 2015. Indonesianjournalofcancer.or.Id, 11(2), 55–60. Retrieved from http://www.indonesianjournalofcancer.or.id/e-journal/index.php/ijoc/article/view/502
Harris, J. R., Lippman, M. E., Morrow, M., & Osborne, C. K. (2014). Evaluation After Primary Therapy and Management of Reccurent Breast Cancer. In Diseases of the breast: Fifth edition (1st ed., p. 871). London: Wolters Kluwer Health Adis (ESP).
Kristanto, A. D., & Kahija, Y. F. La. (2017). PENGALAMAN COPING TERHADAP DIAGNOSIS KANKER PADA PENDERITA USIA KERJA DI RUMAH SAKIT MARGONO SOEKARJO PURWOKERTO. Jurnal Empati, 6(2), 1–9. Retrieved from https://ejournal3.undip.ac.id/index.php/empati/article/viewFile/19723/18656
Pritom, A. I., Munshi, M. A. R., Sabab, S. A., & Shihab, S. (2016). Predicting Breast Cancer Recurrence Uusing Effective Classification and Feature Selection Technique. In 2016 19th International Conference on Computer and Information Technology (ICCIT) (pp. 310–314). Dhaka: IEEE. https://doi.org/10.1109/ICCITECHN.2016.7860215
Rizqiah, A. R., & Subekti, A. (2018). PREDIKSI KEKAMBUHAN KANKER PAYUDARA DENGAN ALGORITMA C4.5. Jakarta.
WHO (2018, February 01). Cancer. July 20, 2018. http://www.who.int/news-room/fact-sheets/detail/cancer
Published
2018-09-15
How to Cite
Rizqiah, A., & Subekti, A. (2018). PREDIKSI KEKAMBUHAN KANKER PAYUDARA DENGAN ALGORITMA C4.5. Jurnal Techno Nusa Mandiri, 15(2), 107-114. https://doi.org/10.33480/techno.v15i2.19
Section
Articles
Copyright (c) 2018 Ai Rita Rizqiah & Agus Subekti
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