ANALISIS DISKRIMINAN KUADRATIK ROBUST DENGAN ESTIMATOR MINIMUM COVARIANCE DETERMINANT (MCD)NIM.: 22106010083Fernanda Devita SukmaauthorDisparities in poverty levels among regencies/cities in East Java Province require a classification method capable of accurately grouping regions to support the formulation of appropriately targeted policies. Quadratic Discriminant Analysis (QDA) is a multivariate classification method that accommodates differences in covariance matrices among groups; however, its parameter estimation is sensitive to the presence of outliers. This study aims to compare the classification performance of classical QDA and robust QDA using the Minimum Covariance Determinant (MCD) estimator in classifying the poverty levels of 38 regencies/cities in East Java Province in 2024 based on five socioeconomic indicators. The analysis consisted of multivariate normality testing using the Henze–Zirkler test, outlier detection using Mahalanobis distance, construction of classical QDA and QDA Robust-MCD models, and model evaluation using the Apparent Error Rate (APER) and classification accuracy. The results showed that the data did not satisfy the multivariate normality assumption, and three outlier observations were identified among the 38 regencies/cities analyzed. Classical QDA achieved a classification accuracy of 78.95% with an APER of 0.2105, whereas QDA Robust-MCD achieved an accuracy of 71.05% with an APER of 0.2895. The comparison results indicate that classical QDA provided better classification performance than QDA Robust-MCD based on both classification accuracy and APER. Thus, for the data analyzed in this study, the use of the MCD estimator did not result in improved classification performance compared with classical QDA.515.6 Metode Analitik - Matematika2026-08-13UIN SUNAN KALIJAGA YOGYAKARTA;FAKULTAS SAINS DAN TEKNOLOGIThesis