Analisis Perbandingan Akurasi Algoritma Naïve Bayes Dan C4.5 untuk Klasifikasi Diabetes

Abstract

Diabetes is a metabolic disease in which blood sugar rises high. If blood sugar is not controlled properly, it can cause a variety of critical diseases, one of which is diabetes. The purpose of this study was to find out the results of comparing the performance values of Naïve Bayes and C4.5 algorithms with 7 different scenarios in the classification of diabetes that will be tested for accuracy, precision, and recall performance. The method used in this study is descriptive, and the source of skunder data obtained from the data of diabetic patients available on Kaggle with the format .csv issued by Ishan Dutta as many as 520 data and 17 fields. The tool used for data analysis is Rapidminer for the process of classification and performance testing of Naïve Bayes algorithm and C4.5 Algorithm. Our results showed that the C4.5 algorithm (scenario 4) had good results in the classification of diabetes compared to Naïve Bayes' algorithm (scenario 2) where the performance of the C4.5 algorithm had an accuracy of 99.03%, precision 100%, and recall 98.18%.