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, Indonesia
Wahyuning Tyas Saraswati, Amikom Purwokerto University, Indonesia
Rizky Firman Ardiansyah, Amikom Purwokerto University, Indonesia
Erik Halma Rouf, Amikom Purwokerto University, Indonesia
Adhi Pratama, Amikom Purwokerto University, Indonesia

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

Downloads

Download data is not yet available.

References

Dewi, S. (2019). Komparasi Metode Algoritma Data Mining pada Prediksi Uji Kelayakan Credit Approval pada Calon Nasabah Kredit Perbankan. Jurnal Khatulistiwa Informatika, 7(1), 59–65. https://doi.org/10.31294/jki.v7i1.5744

Gultom, F., & Simanjuntak, T. (2021). Prediksi Tingkat Kelancaran Pembayaran Kredit Bank Dengan Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 4(2), 98–102. https://doi.org/10.46880/jmika.vol4no2.pp98-102

Harlina, S., Suryani, S., & Oton Kadang, M. (2022). Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi kelayakan Calon Nasabah Kredit Berbasis Web. Seminar Nasional Teknik Elektro, Informatika Dan Sistem Informasi, 1(1). https://doi.org/10.35842/sintaks.v1i1.18

Khatib, J., & Dalam, S. (2022). Indonesian Journal of Computer Science. 11(1), 635–644.

Kurniawan, D. (2008). Regresi linier.

Muryono, T. T., & Irwansyah, I. (2020). Implementasi Data Mining Untuk Menentukan Kelayakan Pemberian Kredit Dengan Menggunakan Algoritma K-Nearest Neighbors (K-Nn). Infotech: Journal of Technology Information, 6(1), 43–48. https://doi.org/10.37365/jti.v6i1.78

Muryono, T. T., Taufik, A., & Irwansyah, I. (2021). Perbandingan Algoritma K-Nearest Neighbor, Decision Tree, Dan Naive Bayes Untuk Menentukan Kelayakan Pemberian Kredit. Infotech: Journal of Technology Information, 7(1), 35–40. https://doi.org/10.37365/jti.v7i1.104

Neighbor, K., Kurniawan, Y. I., Barokah, T. I., Soedirman, U. J., Surakarta, U. M., Prof, J., & Boenjamin, H. R. (2020). Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan. 22(1), 73–82.

Nuraeni, N. (2017). Penentuan Kelayakan Kredit Dengan Algoritma Naïve Bayes Classifier : Studi Kasus Bank Mayapada Mitra Usaha Cabang PGC. III(1), 9–15.

Prawira, A., Arisandi, D., & Sutrisno, T. (2022). Penerapan Algoritma Naive Bayes dan Multiple Linear Regression Untuk Prediksi Status dan Plafon Kredit (Studi Kasus: Bank ABC). Journal on Education, 5(1), 1075–1087. https://doi.org/10.31004/joe.v5i1.720

Retnosari, R. (2021). Analisa Kelayakan Kredit Usaha Mikro Berjalan pada Perbankan dengan Metode Naive Bayes. PROSISKO: Jurnal Pengembangan Riset Dan Observasi Sistem Komputer, 8(1), 53–59.

Rifqo, M. H., & Wijaya, A. (2017). Implementasi Algoritma Naive Bayes Dalam Penentuan Pemberian Kredit. Pseudocode, 4(2), 120–128. https://doi.org/10.33369/pseudocode.4.2.120-128

Shalev-Shwartz, S., & Ben-David, S. (2014). Nearest Neighbor. Understanding Machine Learning, 7(1), 219–227. https://doi.org/10.1017/cbo9781107298019.020

Tampubolon, L. P. D. (n.d.). Efektifitas Faktor Penentu Putusan Penilaian Kelayakan Kredit Berbasis Data Mining. 1057–1064.

Wahyuningsih, S., Utari, D. R., Luhur, U. B., Tree, D., & Validation, K. (2018). Perbandingan Metode K-Nearest Neighbor , Naïve Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit. 8–9.