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	foaf:primaryTopic <http://digilib.uin-suka.ac.id/id/eprint/79783>;
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	dct:hasPart <https://digilib.uin-suka.ac.id/id/eprint/79783/1/22106010083_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf>;
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	rdfs:label "ANALISIS DISKRIMINAN KUADRATIK ROBUST DENGAN ESTIMATOR MINIMUM COVARIANCE DETERMINANT (MCD) (Text)"^^xsd:string .

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		ep:Document;
	rdfs:label "ANALISIS DISKRIMINAN KUADRATIK ROBUST DENGAN ESTIMATOR MINIMUM COVARIANCE DETERMINANT (MCD) (Text)"^^xsd:string .

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	rdf:_1 <http://digilib.uin-suka.ac.id/id/person/ext-eb8b69dfa10c48223f22787ec5309531> .

<http://digilib.uin-suka.ac.id/id/eprint/79783>
	bibo:abstract "Disparities in poverty levels among regencies/cities in East Java Province require a\r\nclassification method capable of accurately grouping regions to support the\r\nformulation of appropriately targeted policies. Quadratic Discriminant Analysis\r\n(QDA) is a multivariate classification method that accommodates differences in\r\ncovariance matrices among groups; however, its parameter estimation is sensitive\r\nto the presence of outliers. This study aims to compare the classification\r\nperformance of classical QDA and robust QDA using the Minimum Covariance\r\nDeterminant (MCD) estimator in classifying the poverty levels of 38\r\nregencies/cities in East Java Province in 2024 based on five socioeconomic\r\nindicators. The analysis consisted of multivariate normality testing using the\r\nHenze–Zirkler test, outlier detection using Mahalanobis distance, construction of\r\nclassical QDA and QDA Robust-MCD models, and model evaluation using the\r\nApparent Error Rate (APER) and classification accuracy. The results showed that\r\nthe data did not satisfy the multivariate normality assumption, and three outlier\r\nobservations were identified among the 38 regencies/cities analyzed. Classical\r\nQDA achieved a classification accuracy of 78.95% with an APER of 0.2105,\r\nwhereas QDA Robust-MCD achieved an accuracy of 71.05% with an APER of\r\n0.2895. The comparison results indicate that classical QDA provided better\r\nclassification performance than QDA Robust-MCD based on both classification\r\naccuracy and APER. Thus, for the data analyzed in this study, the use of the MCD\r\nestimator did not result in improved classification performance compared with\r\nclassical QDA."^^xsd:string;
	bibo:authorList <http://digilib.uin-suka.ac.id/id/eprint/79783#authors>;
	bibo:status <http://purl.org/ontology/bibo/status/published>;
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	dct:creator <http://digilib.uin-suka.ac.id/id/person/ext-eb8b69dfa10c48223f22787ec5309531>;
	dct:date "2026-08-13";
	dct:isPartOf <http://digilib.uin-suka.ac.id/id/repository>;
	dct:issuer <http://digilib.uin-suka.ac.id/id/org/ext-1ea96a2503a70ef7fcbae7670153192b>,
		<http://digilib.uin-suka.ac.id/id/org/ext-008c8b62ba5ae76ed4e62cd13ae8626e>;
	dct:subject <http://digilib.uin-suka.ac.id/id/subject/515.6>;
	dct:title "ANALISIS DISKRIMINAN KUADRATIK ROBUST DENGAN ESTIMATOR MINIMUM COVARIANCE DETERMINANT (MCD)"^^xsd:string;
	ep:hasDocument <http://digilib.uin-suka.ac.id/id/document/1088054>,
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		ep:ThesisEPrint;
	rdfs:seeAlso <https://digilib.uin-suka.ac.id/id/eprint/79783/> .

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	rdf:type skos:Concept;
	skos:prefLabel "515.6 Metode Analitik - Matematika" .

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