Algoritma Naïve Bayes Dengan Backward Elimination Pada Dataset Breast Cancer
DOI:
https://doi.org/10.31599/tzsb5v61Kata Kunci:
Backward Elimination, Breast Cancer, Classification, Naïve Bayes, Split ValidationAbstrak
Cancer is a type of disease that is not recognized by most people, because some people affected by this disease do not know about cancer itself and do not do early detection of cancer, as a result most cancers are found at an advanced stage and are difficult to treat, thus placing a large burden on cancer sufferers. . Early detection of cancer, especially breast cancer is very important to do to overcome the very high risk of death in women caused by breast cancer. This study aims to help classify breast cancer based on data from routine patient examinations which are summarized in the coimbra breast cancer dataset and this data was donated to the UCI machine learning repository in 2018. The method used in the classification process in this study is backward elimination modeling for optimization accuracy as well as the naive Bayes algorithm and split validation validation to validate the model. The results of this study show an accuracy of 77.14%. These results indicate that the results of this study are good enough to help classify breast cancer.
Unduhan
Referensi
Andini, A., & Putri, D. F. A. (2018). Sistem Pakar Diagnosa Penyakit Tuberculosis Menggunakan Certainty Factor, (672014245), 1–12.
Anggraini, R. A., Widagdo, G., Budi, A. S., & Qomaruddin, M. (2019). Penerapan Data Mining Classification untuk Data Blogger Menggunakan Metode Naïve Bayes. Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 7(1), 47. https://doi.org/10.26418/justin.v7i1.30211
Drajana, I. C. R. (2017). Metode Support Vector Machine Dan Forward Selection Prediksi Pembayaran Pembelian Bahan Baku Kopra. ILKOM Jurnal Ilmiah, 9(2), 116–123. https://doi.org/10.33096/ilkom.v9i2.134.116-123
Fijri, A. L., & Rustam, Z. (2018). Comparison between Fuzzy Kernel C-Means and Sparse Learning Fuzzy C-Means for Breast Cancer Clustering. Proceedings of ICAITI 2018 - 1st International Conference on Applied Information Technology and Innovation: Toward A New Paradigm for the Design of Assistive Technology in Smart Home Care, (4), 158–161. https://doi.org/10.1109/ICAITI.2018.8686707
Herlawati, H., & Handayanto, R. T. (2020). Penggunaan Matlab dan Python dalam Klasterisasi Data. Jurnal Kajian Ilmiah, 20(1), 103–118. https://doi.org/10.31599/jki.v20i1.85
Kamagi, D. H., & Hansun, S. (2016). A robust active stabilization technique for dc microgrids with tightly controlled loads. Proceedings - 2016 IEEE International Power Electronics and Motion Control Conference, PEMC 2016, VI(1), 254–260. https://doi.org/10.1109/EPEPEMC.2016.7752007
Maryam, M., & Ariono, H. W. (2022). Sistem Pakar Pengklasifikasi Stadium Kanker Serviks Berbasis Mobile Menggunakan Metode Decision Tree. Jurnal Kajian Ilmiah, 22(3), 267–278. https://doi.org/10.31599/jki.v22i3.1368
Moriesta, E., Selviani, & Ibrahim, A. (2017). Analisis Penyaringan Email Spam Menggunakan Metode Naive Bayes. Prosiding Annual Research Seminar 2017, 3(1), 45–48.
Patricio, M., Pereira, J., Crisosteomo, J., Matafome, P., Seica, R., Caramelo, F., & Gomes, M. (2022). Uci Repository. Retrieved from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra
Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. (2018). Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer, 18(1), 1–8. https://doi.org/10.1186/s12885-017-3877-1
Polat, K., & Senturk, U. (2018). A Novel ML Approach to Prediction of Breast Cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier. ISMSIT 2018 - 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings. https://doi.org/10.1109/ISMSIT.2018.8567245
Priatna, W., Purnomo, R., & Putra, T. D. (2021). Implementasi Deep Learning Untuk Rekomendasi Aplikasi E-learning Yang Tepat Untuk Pembelajaran jarak jauh. Jurnal Kajian Ilmiah, 21(3), 261–274. https://doi.org/10.31599/jki.v21i3.521
Safutra, A. R., & Prabowo, D. W. (2016). Diagnosis Penyakit Kanker Payudara Menggunakan Metode Naive Bayes Berbasis Desktop. Jurnal Penelitian Dosen FIKOM (UNDA), 6(1), 1–6.
Sardouk, F., Duru, A. D., Bayat, O., & others. (2019). Classification of breast cancer using data mining. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 51(1), 38–46.
_-_Copy1.jpg)
