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Sistem Pendukung Keputusan Klinis dengan Memanfaatkan Jaringan Syaraf Tiruan Untuk Mendeteksi Stadium Penderita Kanker Paru-Paru Jenis Karsinoma Bukan Sel Kecil

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 5 No. 2 (2011)

DOI
10.22146/ijccs.2016
ISSN
2460-7258, 1978-1520
Published
2011-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

Collaboration network of the authors

36 authors · 57 co-authorship links · 16 across different organisations

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

  • Universitas Gadjah Mada
  • IPB University
  • Universitas Lamappapoleonro
  • Diponegoro University
  • Universitas 'Aisyiyah Surakarta
  • Universitas Amikom Yogyakarta
  • Yogyakarta State University
  • 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
Retantyo WardoyoUniversitas Gadjah Mada6
Aina MusdholifahUniversitas Gadjah Mada2
Agus HarjokoUniversitas Gadjah Mada2
Sri NurdiatiIPB University1
Jati Sasongko WibowoAffiliation unknown1
Aridhanyati ArifinAffiliation unknown1
Jamila JamilaAffiliation unknown1
Rita WiryasaputraUniversitas Gadjah Mada1
Sri Fitri WahyuniAffiliation unknown1
Sherly JayantiAffiliation unknown1
Salahuddin SalahuddinUniversitas Gadjah Mada1
Tedy RismawanUniversitas Gadjah Mada1
Muhammad Lhsan SaritaAffiliation unknown1
Rusdi EfendiAffiliation unknown1
Rukli RukliUniversitas Lamappapoleonro1
Khadijah KhadijahDiponegoro University1
Jani KusantiUniversitas 'Aisyiyah Surakarta1
Hence Beedwel LumentutAffiliation unknown1
Annisa MaulianiUniversitas Gadjah Mada1
Eka WahyudiAffiliation unknown1
Ari Kusuma WardanaUniversitas Gadjah Mada1
Maulida Ayu FitrianiUniversitas Gadjah Mada1
Achmad Siddik FathoniUniversitas Gadjah Mada1
Agus SihabuddinUniversitas Gadjah Mada1
Ainil MardiahUniversitas Gadjah Mada1
Azhari SubanarAffiliation unknown1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Subanar SubanarUniversitas Gadjah Mada1
Azhari AzhariUniversitas Gadjah Mada1
Sri AndayaniUniversitas Gadjah Mada1
Djemari MardapiYogyakarta State University1
Enny Itje SelaUniversitas Teknologi Yogyakarta1
Munakhir MudjosemediUniversitas Gadjah Mada1
Joan Angelina WidiansMulawarman University1

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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Sistem Pendukung Keputusan Klinis dengan Memanfaatkan Jaringan Syaraf Tiruan Untuk Mendeteksi Stadium Penderita Kanker Paru-Paru Jenis Karsinoma Bukan Sel Kecil</dc:title>
	<dc:creator>Kurniawan, Rio</dc:creator>
	<dc:creator>Hartati, Sri</dc:creator>
	<dc:description xml:lang="en-US">Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is  part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">IndoCEISS</dc:contributor>
	<dc:date>2011-07-31</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/2016</dc:identifier>
	<dc:identifier>10.22146/ijccs.2016</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 5, No 2 (2011): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/2016/1820</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2011 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>