A classification system for jamu efficacy based on formula using support vector machine and k-means algorithm as a feature selection

Abstract

Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. 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) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research that using SVM method without feature selection. This study used variances to evaluate the results of clustering. The total of 3138 data herbs and 465 plant species were grouped into 100 clusters with the variance of 0.0094. The managed group succesfully reduced the data dimension into 3047 of jamu sample and 236 species of herbs and plants as features. The result of SVM classification using feature selection yielded the accuracy of 71.5%.