PERBANDINGAN ALGORITMA KLASIFIKASI DATA MINING MODEL C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT DIABETES - Fatmawati Fatmawati (Jurnal Techno Nusa Mandiri, 2016)
Diabetes is one of the deadly disease, high-risk factors in families that cause diabetes because fat people who do not do physical exercise, and those who do not have a healthy lifestyle and diet excess of what is needed by the body. Based on the history data diabetics can be made on the prediction of diabetes that can help health professionals. Classification is one of data mining techniques that can be used to help predict. Classification can be done with that Decision Tree algorithm C4.5 and Naive Bayes. This study aims to classify and apply data mining classification. Results of data classification in the evaluation using the Confusion Matrix and ROC curve to determine the level of accuracy results using algorithms Decision Tree that is equal to 73.30% and the AUC of the ROC curve was 0733 while the algorithm Naive Bayes amounted to 75.13% AUC values of the ROC curve of 0.810, so it can be said that the algorithm Naive Bayes has the result of a good predictor in predicting diabetes patient.