PERBANDINGAN ANALISIS DISKRIMINAN LINIER KLASIK DAN ANALISIS DISKRIMINAN LINIER ROBUST UNTUK PENGKLASIFIKASIAN KESEJAHTERAAN MASYARAKAT KABUPATEN/KOTA DI JAWA TENGAH - Kartikawati, Ana; Mukid, Moch. Abdul; Ispriyanti, Dwi (Jurnal Gaussian, 2013)
Discriminant analysis is a statistics method which is used to classify an individual or object into certain group which has determined based on its independent variables. Discriminant analysis that commonly used is classical discriminant analysis which consist of classical linear discriminant analysis and classical quadratic discriminant analysis. In classical linear discriminant analysis there are two assumptions to be fulfilled i.e. independent variables have to be normal multivariate distributed and the covariance matrix from the two observed objects should be the same. Classical discriminant analysis cannot work properly if the data which being analyzed consists of many outliers. In order to make discriminant analysis works optimally within the classification though in the condition of data which contains of many outliers, robust estimator is needed. The robust discriminant analysis is used to get the high classification accuracy for data which contains of many outliers. Fast-MCD estimator is one of the robust estimators which is aimed to get the smallest determinant of covariance matrices. The robust linear discriminant analysis with fast-MCD method in this graduating paper is implemented to determine the prosperity status of the people in the regencies or towns in Central Java. The total proportion of classification accuracy using robust linear discriminant analysis method on the data of Central Java people prosperity is 77.14 percent. It is equal with the result from classic linear discriminant analysis which is also 77.14 percent. It is caused by the few amount of outlier on the data of Central Java people prosperity.