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Klasifikasi Nilai Kelayakan Calon Debitur Baru Menggunakan Decision Tree C4.5

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 11 No. 1 (2017) · pp. 43–54

DOI
10.22146/ijccs.15946
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

In an effort to improve the quality of customer service, especially in terms of feasibility assessment of borrowers due to the increasing number of new prospective borrowers loans financing the purchase of a motor vehicle, then the company needs a decision making tool allowing you to easily and quickly estimate Where the debtor is able to pay off the loans.This study discusses the process generates C4.5 decision tree algorithm and utilizing the learning group of debtor financing dataset motorcycle. The decision tree is then interpreted into the form of decision rules that can be understood and used as a reference in processing the data of borrowers in determining the feasibility of prospective new borrowers. Feasibility value refers to the value of the destination parameter credit status. If the value of the credit is paid off status mean estimated prospective borrower is able to repay the loan in question, but if the credit status parameters estimated worth pull means candidates concerned debtor is unable to pay loans..System testing is done by comparing the results of the testing data by learning data in three scenarios with the decision that the data is valid at over 70% for all case scenarios. Moreover, in generated tree and generate rules takes fairly quickly, which is no more than 15 minutes for each test scenario

Keywords

Collaboration network of the authors

6 authors · 6 co-authorship links · 2 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 Muhammadiyah Malang
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Silviani E RumagitUniversitas Gadjah Mada1
Nirwana HendrastutyUniversitas Gadjah Mada1
Aminudin AminudinUniversitas Muhammadiyah Malang1
Baaras AhmadUniversitas Muhammadiyah Malang1

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">Klasifikasi Nilai Kelayakan Calon Debitur Baru  Menggunakan Decision Tree C4.5</dc:title>
	<dc:creator>Hermanto, Bambang</dc:creator>
	<dc:creator>SN, Azhari</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">credit, C4.5, Decision tree</dc:subject>
	<dc:description xml:lang="en-US">In an effort to improve the quality of customer service, especially in terms of feasibility assessment of borrowers due to the increasing number of new prospective borrowers loans financing the purchase of a motor vehicle, then the company needs a decision making tool allowing you to easily and quickly estimate Where the debtor is able to pay off the loans.This study discusses the process generates C4.5 decision tree algorithm and utilizing the learning group of debtor financing dataset motorcycle. The decision tree is then interpreted into the form of decision rules that can be understood and used as a reference in processing the data of borrowers in determining the feasibility of prospective new borrowers. Feasibility value refers to the value of the destination parameter credit status. If the value of the credit is paid off status mean estimated prospective borrower is able to repay the loan in question, but if the credit status parameters estimated worth pull means candidates concerned debtor is unable to pay loans..System testing is done by comparing the results of the testing data by learning data in three scenarios with the decision that the data is valid at over 70% for all case scenarios. Moreover, in generated tree  and generate rules takes fairly quickly, which is no more than 15 minutes for each test scenario</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>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/15946</dc:identifier>
	<dc:identifier>10.22146/ijccs.15946</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 11, No 1 (2017): January; 43-54</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/15946/11721</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
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