Klasifikasi Nilai Kelayakan Calon Debitur Baru Menggunakan Decision Tree C4.5
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
Penulis: Bambang Hermanto
Kode Jurnal: jptinformatikadd170102
