KLASIFIKASI MACHINE LEARNING UNTUK ANEMIA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN RANDOM FOREST

Abstract

 This research involves the classification of anemia using the support vector machine (SVM) and random forest (RF) methods. SVM has the ability to separate two classes of data by finding the optimal hyperplane. On the other hand, Random Forest can handle incomplete or missing data, reduce overfitting, minimize errors, and efficiently handle large training datasets. The classification of anemia in this study uses a 70% training data and 30% testing data split. The accuracy rate of classification using the SVM method is 94%. In comparison, the classification using the RF method achieves an accuracy rate of 98%.