Analisis Metode Ensemble Pada Klasifikasi Penyakit Jantung Berbasis Decision Tree - Mochammad Ilham Aziz; Ahmad Zainul Fanani; Affandy Affandy (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2023)
Heart disease is one disease that is not easy to predict early on and maybe some people are not aware that they have the disease. Data obtained by WHO More than 17 million people worldwide died of heart attacks in 2016. If thesymptoms of heart disease or heart attack are known, prevention of heart disease can be anticipated and even minimized mortality. Analysis of heart disease aims to reduce mortality from the disease. In writing this research, a decision tree algorithm method is used, the algorithm still has weaknesses in making prediction accuracy. So we need a way to improve the accuracy of the classification learning outcomes. This study aims to improve the learning outcomes of heart disease classification by using ensemble learning methods, namely Boostrap Aggregating (Bagging) and Adaptive Boosting (Adaboost). Both methods were tested by predicting deaths caused by heart disease.