PENERAPAN PETA KENDALI T^2 HOTELLING ALGORITMA FAST MINIMUM COVARIANCE DETERMINANT PADA PENGENDALIAN KUALITAS BAWANG MERAH VARIETAS LEMBAH PALU

Abstract

The physical condition of the Palu Valley shallots variety greatly affects the quality of the fried onions that are obtained. The poor quality of shallots will affect the product that will sale by the farmers. Therefore, it is necessary to monitor by conducting the quality control analysis of the physical condition of shallots. In this study we use the quality control method of the  Hotelling control map with the fast-MCD algorithm. This method is used because the outlier in the data to be analyzed. The purpose of this study is to produce average vector estimates and variance-covariance matrix estimates in the formation of the  Hotelling control map. From the calculation by using the mean vector and the variant-covariant matrix with fast-MCD estimation, 93 data were obtained that experienced out of control on the  Hotelling control map with the fast-MCD algorithm where the observations that experienced out of control were more than the usual of  Hotelling control map. This shows that the  Hotelling control map with the fast-MCD algorithm is more effective in detecting observations which contain outliers. The value of the multivariate  process capability analysis is less than one showing the process is uncapable.