Abstract
Classification analysis is a type of analysis for group prediction towards existing groups. There are several classification methods, which are developed, based on the characteristics of the data. Soft Independent Modelling of Class Analogies (SIMCA), is a method that applies a Classical Principal Component Analysis (CPCA) towards every single group. CPCA based on covariance matrix is very sensitive toward outliers and then we use a Robust Principal Component Analysis (ROBPCA) which produces a principal component that will not be affected by outliers. SIMCA is applying ROBPCA as a beginning for SIMCA classification which will be called Robust SIMCA (RSIMCA). Simulation data is used in this research. Simulation data consist of three different scenarios of simulation which are Scenario I, II, and III. The average of misclassification of RSIMCA in all scenarios tends to be stable and smaller when compared to SIMCA. It also revealed that the misclassification from SIMCA are significantly smaller than the misclassification from RSIMCA.
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