Aplikasi Klasifikasi Kolektibilitas Kredit Pemilikan Rumah (KPR) Menggunakan Decision Tree C5.0

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APPLICATION CLASSIFICATION KPR COLLECTIBILITY BY USING
DECISION TREE C5.0

ABSTRACT

Number of bad loan/credit that happened make the bank difficulties to predict the
extent to which the debitur making payment by credit as well as determining which
clients could potentially reach the bad debts at a later given the large number of clients
who did a wide variety of credit one of which is KPR. Decision C5.0 is data mining
techniques as to transform data into decision tree rules and rules of decision. This
method uses a tree structure which present the attribute, the value of attribute and
class. Attribute with the value of the highest entropy as the root or the the root of the
decision tree. From the results of testing on data bank of KPR note that the attributes
value is the highest gain value is the amount of arrears become the most influential
attribute against predictions of KPR. The accuracy of the data obtained is 99,16% and
the error rate is 0,83%.

Key words : Data mining, Decision Tree C5.0, KPR.

Universitas Sumatera Utara