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Open access Jurnal SINTA 2

Case-Based Reasoning untuk Diagnosis Penyakit Jantung

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 11 No. 1 (2017) · pp. 1–10

DOI
10.22146/ijccs.15523
ISSN
2460-7258, 1978-1520
Published
2017-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Case Based Reasoning (CBR) is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR for diagnosing heart disease. The diagnosis process is done by inserting new cases containing symptoms into the system, then the similarity value calculation between cases uses the nearest neighbor method similarity, minkowski distance similarity and euclidean distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold <0.80, the case will be revised by experts. Revised successful cases are stored to add the systemknowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%

Keywords

Collaboration network of the authors

31 authors · 42 co-authorship links · 7 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
  • 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 Mada3
Aina MusdholifahUniversitas Gadjah Mada2
Sri NurdiatiIPB University1
Jati Sasongko WibowoAffiliation unknown1
Aridhanyati ArifinAffiliation unknown1
Jamila JamilaAffiliation unknown1
Rio KurniawanUniversitas Gadjah Mada1
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
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
Agus HarjokoUniversitas Gadjah Mada1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Subanar SubanarUniversitas Gadjah Mada1
Azhari AzhariAffiliation 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://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">Case-Based Reasoning untuk Diagnosis Penyakit Jantung</dc:title>
	<dc:creator>Wahyudi, Eka</dc:creator>
	<dc:creator>Hartati, Sri</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">case-based reasoning; nearest neighbor similarity; minkowski distance similarity; euclidean distance similarity</dc:subject>
	<dc:description xml:lang="en-US">Case Based Reasoning (CBR) is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR  for diagnosing heart disease. The diagnosis process  is done by inserting new cases containing symptoms into the system, then  the similarity value calculation between cases  uses the nearest neighbor method similarity, minkowski distance similarity and euclidean distance similarity.            Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold &lt;0.80, the case will be revised by experts. Revised successful cases are stored to add the systemknowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis.            The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2017-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/15523</dc:identifier>
	<dc:identifier>10.22146/ijccs.15523</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 11, No 1 (2017): January; 1-10</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/15523/11717</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 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>
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