Klasifikasi dengan Analisis Komponen Utama Kernel

dc.contributor.advisor	Siswadi
dc.contributor.advisor	Bakhtiar, Toni
dc.contributor.author	Ustaza, Wirdania
dc.date.accessioned	2014-06-12T01:16:26Z
dc.date.available	2014-06-12T01:16:26Z
dc.date.issued	2013
dc.identifier.uri	http://repository.ipb.ac.id/handle/123456789/69097
dc.description.abstract	Principal component analysis (PCA) is a special case of the kernel PCA with linear kernel function. The aim of this study is to resolve the data problem that is not linearly separated and to classify an object into a group by using kernel PCA to obtain the smallest classification error. Group classification using kernel PCA is performed by the linear and Gaussian kernel function. The result of the study shows for wine recognition data with the linear and Gaussian (parameter 2.5) kernel function produces 30.889% and 17.416% classification error, respectively	en
dc.language.iso	id
dc.title	Klasifikasi dengan Analisis Komponen Utama Kernel	en
dc.subject.keyword	classification error	en
dc.subject.keyword	kernel	en
dc.subject.keyword	principal component analysis	en