Support Vector Machine: Classification of Trade Balance of Provincies in Indonesia Based on Gross Regional Domestic Product and Large Trade Price Index in 2023

Abstract

The aim of this research is to classify Indonesia's trade balance data using the SVM (Support Vector Machine) method with two features, namely Gross Regional Domestic Product (X1) and Wholesale Price Index (X2). Classification is carried out by comparing two types of kernels, namely polynomial kernels and RBF (Radial Basis Function) kernels. Equality Hyperplaneobtained from the polynomial kernel is: . The Hyperplane equation obtained from the RBF kernel is:  Experimental results show that classification with polynomial kernels provides better performance than RBF kernels. This can be seen in the evaluation results which show that the Polynomial kernel has an average model goodness of 75.93% and for the RBF kernel the average model goodness is 74.07%. Leave One Out cross validation (LOOCV) simulation for training data obtained an average accuracy of 76.67% for the polynomial kernel and 66.67% for the RBF kernel. This shows that in this classification context, kernel polynomials are more effective in separating data classes.