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Jurnal SINTA 2

KLASIFIKASI CITRA DENGAN POHON KEPUTUSAN

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 7 No. 2 (2008) · pp. 55–64

DOI
10.12962/j24068535.v7i2.a173
ISSN
2406-8535, 1412-6389
Published
2008-07-01
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Image classification can be done by using attribute of text that come along with the image, such as file name, size, or creator. Image classification also can be done base on visual content of the image. In this research, we implement a image classification model base on image visual content. The image classification is based on decision tree method that adapt C4.5 algorithm. The decision variable used in the decision tree generation process is image visual features, i.e. color moment order-1, color moment order-2, color moment order-3, entropy, energy, contrast, and homogeneity. The result of this research is an application that can classified image base on the knowledge of the previous classification cases. Keywords: image classification, decision tree, C4.5 algorithm

Collaboration network of the authors

40 authors · 63 co-authorship links · 14 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Universitas Amikom Yogyakarta
  • Universitas Stikubank
  • University of Indonesia
  • Universidad de Murcia
  • IPB University
  • State University of Semarang
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Aina MusdholifahUniversitas Gadjah Mada7
Edi WinarkoUniversitas Gadjah Mada7
Khabib MustofaUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Azhari AzhariUniversitas Gadjah Mada3
Arief SetyantoUniversitas Amikom Yogyakarta2
Edy WinarnoUniversitas Stikubank2
Aniati Murni ArymurthyUniversity of Indonesia2
Rahmat BudiarsaUniversitas Gadjah Mada2
Rodrigo Martínez‐BéjarUniversidad de Murcia2
I Made Artha AgastyaUniversitas Amikom Yogyakarta2
Anny Kartika SariUniversitas Gadjah Mada2
Kridalaksana Harsa AwangAffiliation unknown1
Bangun WijayantoUniversitas Gadjah Mada1
Sri NurdiatiIPB University1
Decky HendarsyahUniversitas Gadjah Mada1
Jati Sasongko WibowoAffiliation unknown1
Zaenal AbidinState University of Semarang1
Aridhanyati ArifinAffiliation unknown1
Jamila JamilaAffiliation unknown1
Awaluddin AwaluddinAffiliation unknown1
Rio KurniawanUniversitas Gadjah Mada1
Yustina RetnoAffiliation unknown1
Rita WiryasaputraUniversitas Gadjah Mada1
Sri Fitri WahyuniAffiliation unknown1
Sherly JayantiAffiliation unknown1
Salahuddin SalahuddinUniversitas Gadjah Mada1
Putri Elfa Mas’udiaAffiliation unknown1
Abriyono AbriyonoAffiliation unknown1
I Gede Aris GunadiUniversitas Pendidikan Ganesha1
Rina PramitasariUniversitas Gadjah Mada1
Tedy RismawanUniversitas Gadjah Mada1
Muhammad Lhsan SaritaAffiliation unknown1
Rusdi EfendiAffiliation unknown1
Ema UtamiUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1

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">KLASIFIKASI CITRA DENGAN POHON KEPUTUSAN</dc:title>
	<dc:creator xml:lang="en">Kusrini, Kusrini</dc:creator>
	<dc:creator xml:lang="en">Hartati, Sri</dc:creator>
	<dc:creator xml:lang="en">Wardoyo, Retantyo</dc:creator>
	<dc:creator xml:lang="en">Harjoko, Agus</dc:creator>
	<dc:description xml:lang="en">   Image classification can be done by using attribute of text that come along with the image, such as file name, size, or creator. Image classification also can be done base on visual content of the image. In this research, we implement a image classification model base on image visual content. The image classification is based on decision tree method that adapt C4.5 algorithm. The decision variable used in the decision tree generation process is image visual features, i.e. color moment order-1, color moment order-2, color moment order-3, entropy, energy, contrast, and homogeneity. The result of this research is an application that can classified image base on the knowledge of the previous classification cases.   Keywords: image classification, decision tree, C4.5 algorithm</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2008-07-01</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/173</dc:identifier>
	<dc:identifier>10.12962/j24068535.v7i2.a173</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol 7, No 2, Juli 2008; 55-64</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/173/128</dc:relation>
</oai_dc:dc>