Klasifikasi Penyakit Tuberkulosis Menggunakan Metode Naive Bayes (Studi Kasus: Data Pasien Di Puskesmas Petung Kabupaten Penajam Paser Utara)

Abstract

The Naive Bayes method is one of the data mining methods used in classifying data and predicting future opportunities based on experience or previous data. This method was proposed by British scientist Thomas Bayes using a branch of mathematics known as probability theory. One of the diseases that can be detected using the classification using the Naive Bayes method is Tuberculous (TB). Tuberculous is an infectious respiratory disease caused by the bacterium Mycobacterium Tuberculosis. The purpose of this study was to determine the results and accuracy of the classification of Tuberculous disease using the Naive Bayes method in one of the health service units, namely Puskesmas Petung, Penajam Paser Utara. The results showed that data mining classification using the Naive Bayes method was appropriate in classifying Tuberculous. For training and testing data, divided into 90:10, the accuracy rate is 87.5%, categorized as Excellent Classification. As for the training and testing data divided into 70:30, the accuracy rate is 90.9%, classified as Excellent Classification.