Hasil Analisis Teknik Data Mining dengan Metode Naive Bayes untuk Mendiagnosa Penyakit Kanker Payudara
DOI:
https://doi.org/10.30865/json.v1i2.1766Keywords:
Breast Cancer, Data Mining, Naive Baiyes, Gain Ratio, WekaAbstract
Breast cancer or Mammae Carsinoma is an uncontrolled cell growth in the milk-producing glands (lobular), the gland tract from the lobular to the Breast nipple (ductus), and the breast support tissues that surround the lobular, ductus, vessels Blood and limfe vessels, but does not include breast skin. Research begins by conducting a preprocessing stage, to eliminate missing values. After that the process is imputasi to remove missing values. It then performed a feature selection to see which attribute had a major impact on the data. The last stage is classification with two methods, namely Naïve Bayes. At the end of the study, the method is best to classify the recurrence data of breast cancer patients.
References
R. DAMAYANTI, PENGARUH PELAKSANAAN PEMERIKSAAN PAYUDARA SENDIRI (SADARI) TERHADAP PENGETAHUAN DAN KEMAMPUAN SISWI DALAM UPAYA DETEKSI DINI KANKER PAYUDARA DI SMP NEG.1 SIBULUE KAB. BONE. MAKASSAR, 2017.
A. K. Omega Memed, KORELASI ANTARA C-reactive Protein DAN PARAMETER TROMBOSIT PADA PASIEN KANKER PAYUDARA. SURAKARTA, 2019.
M. F. Kurniawan and J. Nugraha Irawan, “Peningkatan Performa Algoritma Naive Bayes dengan Gain Ratio untuk Klasifikasi Keganasan Kanker Payudara,†2018.
M. F. Kurniawa and Ivandari (last), “KOMPARASI ALGORITMA DATA MINING UNTUK KLASIFIKASI PENYAKIT KANKER PAYUDARA,†2017.
Balqis Aisyah Farahdiba and Yusuf Sulistyo Nugroho, “Klasifikasi Kanker Payudara Menggunakan Algoritma Gain Ratio,†2016.
Ai Rita Rizqiah and Agus Subekti, “PREDIKSI KEKAMBUHAN KANKER PAYUDARA DENGAN ALGORITMA C4.5,†2018.
