Comparison Analysis of SVM Algorithm with Linear Regression in Predicting used Car Prices - Yennimar Yennimar; Kelvin Kelvin; Suwandi Suwandi; Amir Amir (Jurnal Mantik, 2022)
During the high activity , car has become a basic need. On the other hand, the price of new car is getting higher. To meet these needs, people are looking for alternatives by buying used cars. One of the factors to consider when looking for a used car is price. In this study, two algorithms that are quite popular in terms of prediction will be tested, namely the Support Vector Machine algorithm and the Linear Regression algorithm in predicting used car prices. Support Vector Machine is a supervised learning method that analyzes data and recognizes patterns for regression. Support Vector Machine has the ability to solve linear and nonlinear problems. Linear Regression Algorithm is a modeling and analysis of numerical data consisting of one or more independent variables and the value of the dependent variable, with the aim of using regression analysis to estimate the value of the dependent variable based on the value of the independent variable. The result of this research is that the SVM method can perform better than linear regression. SVM can perform kernel-tricks that can handle non-linear data, thus making the non-linear data appear to be linear. but this cannot be done by Linear regression.