Sistem Klasifikasi Khasiat Formula Jamu dengan Metode Support Vector Machine

Abstract

Jamu is made from natural materials such as roots, leaves, wood and fruits. The composition of jamu formula is usually constructed based on empirical data or personal experiences. Jamu has many variations of formula. Thus, the classification of jamu efficacy based on its composition of plants remains an interesting task. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM). This method were compared to those of previous research using Partial Least Squares Discriminant Analysis (PLS-DA). The results show that the SVM method with Radial Basis Function (RBF) kernel using data reduction and balanced dataset obtains higher accuracy than those of PLS-DA.