Analisis Diskriminan Linear Robust Dengan Metode Winsorized Modified One-Step M-Estimator - Mega Selvia Tjahaya; Raupong; Georgina Maria Tinungki (Hasanuddin University, 2022)
Discriminant analysis is a method used to classify an individual (object) into a group. Discriminant analysis is divided into classical linear discriminant analysis and classical quadratic discriminant analysis. Discriminant analysis must fulfilled the assumptions of normality and homogeneity of the variance-covariance matrix, however this method is very sensitive to data contains outliers. Robust linear discriminant analysis with the winsorized modified one-step M-estimator(WMOM) approach is a method that can resolve outliers data. WMOM works by trimming these outliers then replacing the outliers with the highest or lowest value of the remaining data by using criteria trimming MOM. This study aims to obtain a linear robust discriminant function with the WMOM method using the Sn scale on diabetes and prediabetes data for the period December 2016-January 2017. Based on the results of the analysis and discussion of this method, the discriminant function is obtained with a classification error rate of 16.67%. Keywords: Diabetes, One-Step M-estimator, Prediabetes, Robust Linear Discriminant Analysis, Winsorized Modified.