Penerapan LASSO Least Trimmed Squares untuk Mengidentifikasi Peubah yang Berpengaruh Penyebaran Penyakit di Sulawesi 138 Selatan - Randa, Trigarcia Maleachi; Tinungki, Georgina Maria; Sunusi, Nurtiti (Journal of Mathematics, Computations and Statistics, 2023)
Tuberculosis is a chronic infectious disease which is still a public health problem in the world.Indonesia is a country with the third highest tuberculosis burden after India and China. South Sulawesi isone of the provinces that contributes to the high number of tuberculosis cases in Indonesia in 2020. Linearregression analysis can be applied to tuberculosis data to determine the variables that affect the numberof tuberculosis cases in South Sulawesi. Problems that often arise in regression analysis aremulticollinearity problems and outliers in the data. One method that can be used to solve multicollinearityand outlier problems is the LASSO LTS regression. The LASSO LTS regression is a modification of theLASSO regression method based on the LTS estimator of joint regression. The variables in the tuberculosisdata in South Sulawesi have multicollinearity problems and there are outliers, so in this study an approachwith the LASSO LTS method was used to overcome them. The results showed that the LASSO LTS methodcould overcome multicollinearity and outlier problems in estimating regression parameters as evidencedby the highest coefficient of determination of 89.41%.