A classification system for Jamu efficacy based on formula using Support Vector Machine

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