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OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) STUDI KASUS: PENDUGAAN DATA INDEKS KETAHANAN PANGAN TAHUN 2021
Correlation between predictor variables in regression analysis can be a problem or violation of linear regression assumptions that called multicollinearity. Multicollinearity causes the variance to become large, so that the estimation of regression parameters becomes inefficient. Multicollinearity can be detection by considering the variance inflation factor (VIF) value and its correlation coefficient. This problem can be overcome by using Least Absolute Shrinkage and Selection Operator (LASSO) Regression. LASSO will shrink the regression coefficient close to zero or exactly zero, the calculation of which is done with the help of the Least Absolute Regression (LARS) algorithm. In the case study on the Food Security Index data, predictor variables that have high correlation will be gradually entered into the model. The first variable to enter is X4 then continued by X9, X2, X1, X8, X6, X11, X7, X5, X3 up to X10. The results of the coefficient estimation at each stage will produce several regression models. These stages produce several regression models which are then tested by selecting the best Bayesian Information Criterion (BIC) model. The minimum BIC value (0.342) indicates an optimal lambda of 0.059, so the best model is obtained at the thirty-fifth stage. The results of the study show that seven of the eleven predictor variables have an influence on the food security index in 2021.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) STUDI KASUS: PENDUGAAN DATA INDEKS KETAHANAN PANGAN TAHUN 2021 (Text)
OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) STUDI KASUS: PENDUGAAN DATA INDEKS KETAHANAN PANGAN TAHUN 2021 (Other)
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