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Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 1 (2021) · pp. 1–10

DOI
10.22146/ijccs.58919
ISSN
2460-7258, 1978-1520
Published
2021-01-31
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 method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration. This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods. The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance. The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.

Keywords

Collaboration network of the authors

19 authors · 28 co-authorship links · 4 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Sebelas Maret University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Azhari SnUniversitas Gadjah Mada2
Yunita SariUniversitas Gadjah Mada2
Auliya Rahman IsnainUniversitas Teknokrat Indonesia2
Abram Setyo PrabowoUniversitas Gadjah Mada1
Yayang Eluis Bali MawartikaUniversitas Gadjah Mada1
Muhammad Asaduddin HazaziUniversitas Gadjah Mada1
Sigit PriyantaUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Ainil MardiahUniversitas Gadjah Mada1
Riszki Wijayatun PratiwiUniversitas Gadjah Mada1
Sensa Gudya Sauma SyahraUniversitas Gadjah Mada1
Astrid Noviana ParadhitaSebelas Maret University1
Anny Kartika SariUniversitas Gadjah Mada1
Fadila Shely AmaliaUniversitas Gadjah Mada1
Dian EkapratiwiUniversitas Gadjah Mada1

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">Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning</dc:title>
	<dc:creator>Utami, Annisaa</dc:creator>
	<dc:creator>Suyanto, Yohanes</dc:creator>
	<dc:creator>Sihabuddin, Agus</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science;Case Based Reasoning</dc:subject>
	<dc:subject xml:lang="en-US">Case Based Reasoning; Nearest Neighbor; Manhattan Distance; Thesis</dc:subject>
	<dc:description xml:lang="en-US">Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration.               This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods.               The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance.  The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.</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>2021-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/58919</dc:identifier>
	<dc:identifier>10.22146/ijccs.58919</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 1 (2021): 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/58919/30553</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 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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