PENERAPAN ESTIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN AKAIKE INFORMATION CRITERION (AIC)

LINAKSANAN, NIM. 17106010026 (2021) PENERAPAN ESTIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN AKAIKE INFORMATION CRITERION (AIC). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Multicollinearity is a condition where the independent variables are correlated with each other and cause the matrix to be almost singular, so that parameter estimation becomes infinite and it is difficult to estimate it. To overcome the multicollinearity, ridge regression is used by adding the bias constant c to the diagonal of the XtX matrix. The purpose of this study is to compare the Schall’s algorithm and Akaike Information Criterion (AIC) on ridge regression in determining the best model, using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) measurements. The results of this study are that the Schall’s algorithm is the best method in selecting the regression model for the data of the Open Unemployment Rate (TPT) of Central Java Province in 2017 because the MSE and MAPE values of the Schall’s algorithm are smaller than the MSE and MAPE values of the AIC method.

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