Integration of Iterative Dichotomizer 3 and Boosted Decision Tree to Form Credit Scoring Profile

Prasetyo, Alditama Agung; Kristianto, Budhi

doi:10.24167/sisforma.v7i2.2659

Cited by 1 publication

(1 citation statement)

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“…Entropy is the process of using the probability of certain events or attributes to measure class uncertainty [32]. After calculating the entropy value, then we can only calculate the Information Gain method [33].…”

Section: B the Information Gain Methodsmentioning

confidence: 99%

Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability

Herfandi¹,

Zaen²,

Yuliadi³

et al. 2021

JISA

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The customer is the main factor in the running of PT. XYZ. A good understanding of customers is very important for predicting the capability of customers to pay. The implementation of credit collectibility is used to determine the quality of customer credit, one of which is the customer's capability to pay interest and principal on time. While manually, it is very difficult to accurately predict the capability of customer credit payments. Data mining techniques with the Naïve Bayes algorithm were chosen to classify customers to be able to find patterns, analyze and predict, because they have good performance, are efficient, and simple. The Naïve Bayes algorithm has a weakness in terms of sensitivity to many attributes, so the accuracy is low. Based on the problem stated, his study will apply the Information Gain method to select the most influential attribute on the label in order to increase the accuracy of the Naïve Bayes algorithm. This research produces a new dataset with seven attributes: TENOR, SALARY, DOWN PAYMENT, INSTALLMENT, APPROVAL, OTR CLASS, AGE with Labels: Status and Id: Id number based on the Information Gain method. The dataset comparison process with 995 data records showed an increase in accuracy, precision, and AUC using the new dataset compared to the old dataset, but in the t-Test test with an alpha value = 0.05 there is a difference but not significant. In the evaluation process, performance experienced a significant increase in the use of new datasets with the following percentages of performance improvement: accuracy = 8%, precision = 18.42%, recall = 17.65% and AUC= 0.057%. The results of this study obtained AUC of 0.876, accuracy of 87.88%, precision of 61.90%, and recall of 76.47%, and classified into good classification.

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Section: B the Information Gain Methodsmentioning

confidence: 99%