OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA TIMUR TAHUN 2022)

Adelia Disty Nariswari, NIM.: 20106010037 (2024) OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA TIMUR TAHUN 2022). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Linear regression is one of the modeling methods often used in data analysis. However, this model must fulfill several basic assumptions, one of which is the absence of multicollinearity. Multicollinearity occurs when the independent variables have a strong correlation, which can lead to instability and inaccuracy in the estimation of regression coefficients. Multicollinearity identification uses the Variance Inflation Factor (VIF) value or an examination of the correlation coefficient between variables in a regression model. One method that can overcome multicollinearity problems is Least Absolute Shrinkage and Selection Operator (LASSO) regression, which is able to shrink the regression coefficients of highly correlated independent variables to near zero or exactly zero. The Least Angle Regression and Shrinkage (LARS) algorithm is used to determine the regression coefficients, while the selection of the optimal lambda value in LASSO regression is done using the Bayesian Information Criterion (BIC), where the best model is determined based on the lowest BIC value. This research is applied to data on the open unemployment rate in East Java in 2022. The results showed that the LASSO regression would produce 81 lambdas and the optimal lambda was chosen at 0.0499297734 with a BIC value of 0.8010647. The conclusion obtained based on the research that has been done is that three of the five independent variables are included in the model, namely the labor force participation rate, the human development index, and the percentage of poor people.

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