PERBANDINGAN REGRESI RIDGE DAN PRINCIPAL COMPONENT ANALYSIS DALAM MENGATASI MASALAH MULTIKOLINEARITAS - Irwan, Irwan; Hasriani, Hasriani (Teknosains: Media Informasi Sains dan Teknologi, 2016)
Multiple linear regression said to be good if it statistic the assumptions such as: normality assumption, heteroskedastisity, an error does not undergo autocorrelation and not occour multicolinearity. On the assumption that problem often arise in the multiple linear regression assumptions are not fulfilled multicolinearity. Multicollinearity is a condition in which the data of the observations of the independent variables occuror have a relationship that is likely to be high. This study aimed to compare the appropriate method to over come multicollinearity between ridge regression and principal component analysis. Comparison criteria used both methods, the mean square error (MSE) and the coefficient of determination (R2), from the data is the simulation with Microsoft Excel then the analysis was performed, in order to obtain the data first using ridge regression has a value of MSE of 0.02405 and R2 of 82.4%, while the principal component analysis MSE value of 14.14 and R2of 37.5% while the data second using ridge regression MSE has a value of 0.00216 and R2 of 96.9%, while the principal component analysis MSE values of 5.15 and R2 of 69.5%. From these results it can be concluded that ridge regression method is better used.