Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision Tree Algoritma (J48) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda

Irene Lishania (aboratorium Statistika Komputasi FMIPA Universitas Mulawarman)
Rito Goejantoro (aboratorium Statistika Komputasi FMIPA Universitas Mulawarman)
Yuki Novia Nasution (Laboratorium Matematika Komputasi FMIPA Universitas Mulawarman)

Eksponensial
Vol 10 No 2 (2019)

Classification is a technique to form a model of the data that has not been classified, then the model can be used to classify new data. Naive Bayes is a classification using probability method based on the Bayes theorem with a strong assumption of independence. The decision tree algorithm (J48) is an implementation of the algorithm (C4.5) that produces decision trees. In this research, will be compared the results of classification accuracy with the naive Bayes method and the decision tree algorithm (J48) in stroke patients. That is, a person who has stroke will be classified by using the data of patients in Abdul Wahab Sjahranie Samarinda Hospital with 7 factors, namely age, gender, blood pressure, diabetes mellitus, dyslipidemia, uric acid levels and heart disease. The results showed that the decision tree algorithm (J48) method has the higher level of accuracy than the method naive Bayes for stroke classification.