Determining The Loan Feasiblity of Bank Customers Using Naïve Bayes, K-Nearest Neighbors And Linear Regression Algorithms

Aniec Anafisah Pratiwi; Wahyuning Tyas Saraswati; Rizky Firman Ardiansyah; Erik Halma Rouf; Adhi Pratama

Authors

Aniec Anafisah Pratiwi Amikom Purwokerto University
Wahyuning Tyas Saraswati Amikom Purwokerto University
Rizky Firman Ardiansyah Amikom Purwokerto University
Erik Halma Rouf Amikom Purwokerto University
Adhi Pratama Amikom Purwokerto University

Keywords:

KNN, KNIME, linear regression, naïve bayes

Abstract

In the financial industry, lending to customers is one of the core activities in the financial sector which has a significant impact on the economy and business growth. Credit is the provision of money or bills that can be equated with it, based on a loan agreement between banks and other parties that requires the agreement to repay the debt after a certain period of time by providing interest. However, the process within these financial institutions needs to assess the feasibility of granting credit to customers who apply for credit. To facilitate the determination of eligibility for granting credit to customers, an accurate and effective analytical method is needed to help solve problems in determining the eligibility classification for granting credit to customers by applying the Naive Bayes, K-Nearest Neighbors (K-NN) and Linear Regression algorithms. Based on the results of the tests that have been carried out using the three algorithms obtained, the results show an accuracy value on K-NN of 87.837%, calculations using the Naïve Bayes algorithm have an accuracy value of 88.917%, while calculations using the Linear Regression algorithm produce a Mean absolute error value of 6.703. It can be concluded that in bank creditworthiness fraud using the Naïve Bayes algorithm method is more accurate when compared to the K-NN and Linear Regression algorithms

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