Aplikasi Diagnosa Penyakit Tuberculosis Menggunakan Algoritma Naive Bayes
 (*)Amrin Amrin  (Universitas Bina Sarana Informatika, Indonesia)
Hafdiarsya Saiyar (Universitas Bina Sarana Informatika, Indonesia)
(*) Corresponding Author |
Abstract
It is important for doctors to make an early diagnosis of tuberculosis in order to reduce the transmission of the disease to the wider community. In this study, the authors will apply methods of data mining classification, Naïve Bayes to diagnose tuberculosis disease. Based on the performance measurement results of the models using Cross Validation, Confusion Matrix and ROC Curve methods, it is known that Naïve Bayes method with accuracy of 94.18% and under the curva (AUC) value of 0.97. This shows that the models that are produced including the category of classification is very good because it has an AUC value between 0.90-1.00.
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