* Corresponding Author
Hide
Show all
Klasifikasi Nilai Kelayakan Calon Debitur Baru Menggunakan Decision Tree C4.5
https://doi.org/10.22146/ijccs.15946 Bambang Hermanto(1), Azhari SN(2*)
(1)
(2) Universitas Gadjah Mada
(*) Corresponding Author
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
Full Text:
PDFReferences
Berry, Michael J.A dan Gordon S. Linoff., 2004, Data Mining Techniques for Marketing, Sales, Customer Relationship Management, Second Edition, Willey Publishing, Inc.
Chen, Y., Dai, L., dan Chen, X,Q., 2008, GATS-C4.5: An Algorithm for Optimizing Features in Flow Classification, EEE Communications Society subject matter experts for publication in the IEEE CCNC 2008 proceedings., 466-470.
Gorunescu, F., 2011, Data Mining Concept Model and Techniques, Berlin: Springer. ISBN 978-3-642-19720-8
Han, J., and Kamber, M. 2006, Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman. ISBN 13: 978-1-55860-901-3
Jaber, K.M, Abdullah, R., Rashid N., 2012, HDT-HS: A Hybrid Decision Tree/Harmony Search Algorithm for Biological Datasets, 2012 International Conference on Computer dan Information Science (ICCIS), 341-345.
Kornienko, Y., Borisov, A, 2003, Investigation of a Hybrid Algorithm for Decision Tree Generation, IEEE lnrernational Workshop 00 lnfelligem Dah Acquisition and Advanced Coquting System: Techlogy and Applications, Lviu. Ukraine, Institute of Information Technology, Riga Technical University, Latvia, 8-10 September 2003, 63-68
Larose, D.T., 2005, Discovering knowledge in data : An Introduction to Data Mining, JohnWiley and Sons, Inc., New Jersey.
Liu, C., dan Jiang, Q., 2009, Mixed Financial Forecasting Index System Construct and Financial Forecasting Study on the C4.5 Decision Tree, School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai , China
Pressman, R.S., 1997, Software Engineering: A Practitioner’s Approach, The McGraw-Hill Companies, Inc.
Witten, I. H., Frank, E., Hall, M. A., 2011, Data Mining Practical Machine Learning Tools and Techniques (3rd ed). USA: Elsevier
DOI: https://doi.org/10.22146/ijccs.15946
Article Metrics
Abstract views : 4117
| 
views : 9383
Refbacks
- There are currently no refbacks.
Copyright (c) 2017 IJCCS - Indonesian Journal of Computing and Cybernetics Systems
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1