Optimasi Decision Tree Menggunakan Teknik Boosting pada Prediksi Penyakit Diabetes

Abstract

Early detection of diabetes is very important to reduce the consequences caused by the disease. Diabetes is influenced by many factors, so to make a diagnosis requires a complex analysis. The dataset used to analyze the prediction of diabetes is using machine learning algorithm. The machine learning algorithm is used to classify someone with diabetes or not based on the factors that have been set as input. The results of the diagnosis/prediction that are not perfect are caused by many misclassifications. To reduce classification errors, it is proposed to apply decision tree and boosting techniques. The classification algorithm used in this study is Random Forest. The experimental results show that decision tree and boosting techniques and a combination of the two can reduce misclassification in diabetes prediction.