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Jurnal SINTA 2

PENERAPAN METODE ANALISA DISKRIMINAN MAJEMUK DENGAN PENDEKATAN TRANSFORMASI FUKUNAGA KOONTZ

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 7 No. 3 (2009) · pp. 135–142

DOI
10.12962/j24068535.v7i3.a80
ISSN
2406-8535, 1412-6389
Published
2009-01-01
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Linear discriminant analysis is one of method frequently used and developed in the field of pattern recognition. This method tries to find the optimal subspace by maximizing the Fisher Criterion. Application of pattern recognition in highdimensional data and the less number of training samples cause singular within-class distribution matrix. In this paper, we developed Linear Discriminant Analysis method using Fukunaga Koontz Transformation approach to meet the needs of the nonsingular within-class distribution matrix. Based on Fukunaga Koontz Transformation, the entire space of data is decomposed into four subspaces with different discriminant ability (measured by the ratio of eigenvalue). Maximum Fisher Criterion can be identified by linking the ratio of eigenvalue and generalized eigenvalue. Next, this paper will introduce a new method called complex discriminant analysis by transforming the data into intraclass and extraclass then maximize their Bhattacharyya distance. This method is more efficient because it can work even though within-class distribution matrix is singular and between-class distribution matrix is zero.

Collaboration network of the authors

21 authors · 40 co-authorship links · 3 across different organisations

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  • Institut Teknologi Sepuluh Nopember
  • Institut Sains dan Teknologi Terpadu Surabaya
  • Universitas Ma Chung
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yudhi PurwanantoInstitut Teknologi Sepuluh Nopember4
Esther HanajaInstitut Teknologi Sepuluh Nopember2
Wahyu SuadiInstitut Teknologi Sepuluh Nopember2
Sheila AgustiantyAffiliation unknown1
I Ketut Eddy PurnamaInstitut Sains dan Teknologi Terpadu Surabaya1
Arif DjunaidyInstitut Teknologi Sepuluh Nopember1
Adhita PratiwiAffiliation unknown1
Tohari AhmadInstitut Teknologi Sepuluh Nopember1
Rully A. HendrawanAffiliation unknown1
Hendry SetiawanUniversitas Ma Chung1
Aidy IlmyAffiliation unknown1
Aby HerwendoAffiliation unknown1
I Wayan Widi PradnyanaAffiliation unknown1
Sarwosri SarwosriInstitut Teknologi Sepuluh Nopember1
Suhadi LiliInstitut Teknologi Sepuluh Nopember1
Ary Mazharuddin ShiddiqiInstitut Teknologi Sepuluh Nopember1
Muhammad Raihan AninditaInstitut Teknologi Sepuluh Nopember1
Ilham Gurat AdillionInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">PENERAPAN METODE ANALISA DISKRIMINAN MAJEMUK DENGAN PENDEKATAN TRANSFORMASI FUKUNAGA KOONTZ</dc:title>
	<dc:creator xml:lang="en">Soelaiman, Rully</dc:creator>
	<dc:creator xml:lang="en">Anggraini, Wiwik</dc:creator>
	<dc:creator xml:lang="en">Mujahidillah, M</dc:creator>
	<dc:description xml:lang="en">Linear discriminant analysis is one of method frequently used and developed in the field of pattern recognition. This method tries to find the optimal subspace by maximizing the Fisher Criterion. Application of pattern recognition in highdimensional data and the less number of training samples cause singular within-class distribution matrix. In this paper, we developed Linear Discriminant Analysis method using Fukunaga Koontz Transformation approach to meet the needs of the nonsingular within-class distribution matrix. Based on Fukunaga Koontz Transformation, the entire space of data is decomposed into four subspaces with different discriminant ability (measured by the ratio of eigenvalue). Maximum Fisher Criterion can be identified by linking the ratio of eigenvalue and generalized eigenvalue. Next, this paper will introduce a new method called complex discriminant analysis by transforming the data into intraclass and extraclass then maximize their Bhattacharyya distance. This method is more efficient because it can work even though within-class distribution matrix is singular and between-class distribution matrix is zero.</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2009-01-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/80</dc:identifier>
	<dc:identifier>10.12962/j24068535.v7i3.a80</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol 7, No 3, Januari 2009; 135-142</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v7i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/80/76</dc:relation>
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