Analisa Classification Decision Tree C45 dan Naïve Bayes Pada Indikasi Penyakit Diabetes Menggunakan Rapid Miner - Hamzah, Iswadi; Zulham Sitorus; Khairul (Jurnal Nasional Teknologi Komputer, 2024)
In Indonesia, the rate of diabetes sufferers continues to increase, so this is deemed necessary to pay attention to by the Indonesian people in particular, for this reason this research is not the first to be conducted. Predicting diabetes can be done using various methods through various algorithms which are quite diverse, therefore it is necessary to conduct research on the algorithms used. To obtain new information, the Decision Tree algorithm with Naïve Bayes was tested using the Rapid Miner application. This test is carried out on data that has the attribute HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, Heart Diseaseor Attack, Phys Activity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCos, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income. All of these attributes serve as a guide in determining results, so that it can be known that the patient has diabetes.