Comparison of K-Nearest Neighbor, Naive Bayes Classifier, Decision Tree, and Logistic Regression in Classification of Non-Performing Financing
Abstract
The Non-Performing Financing (NPF) indicator of one of the Islamic Banks in Indonesia in the 1st to 3rd quarter of 2021 in a row of 9.69%; 9.97%; 9.46%. The NPF movement tends to improve slightly from time to time but still exceeds the maximum limit stipulated in Bank Indonesia’s Regulation Number 23/2/PBI/2021, which is no more than 5%. This shows that the Islamic bank has a financing performance that can be said to be less good. Preventive steps that can be taken to reduce the NPF ratio in order to improve the health of the bank is to classify prospective financing customers. This research was conducted using the K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree, and Logistics Regression classification methods to predict potential financing customers. The dataset is divided into 80% training and 20% testing. It was found that the best classification result was the Naive Bayes Classifier in the proportion of distribution of 80% training data and 20% testing data with an accuracy value of 84.69%, sensitivity of 58.25%, and specificity of 90.16%.
References
[2] Annur, Haditsah & Efendi Lasulika, Moh. (2019). Klasifikasi Nasabah Kredit Koperasi Menggunakan Algoritma K-Nearest Neighbor. Jurnal Informatika Upgris. 5(2): 126-129.
[3] J, Suntoro. (2019). Data Mining: Algoritme dan Implementasi Menggunakan Bahasa Pemrograman PHP. PT. Elex Komputindo. Jakarta
[4] Sarimuddin et al. (2020). Klasifikasi Data Aging Tunggakan Nasabah Menggunakan Metode Decision Tree pada ULaMM Unit Kolaka. Informatics Journal. 5(1): 26-32.
[5] Alwi, Wahidah., Ermawati, & Husain, Saddam. (2018). Analisis Regresi Logistik Biner untuk Memprediksi Kepuasan Pengunjung pada Rumah Sakit Umum Daerah Majene. Jurnal MSA. 6(1): 20-26.
[6] Ciptohartono, Claudia Clarentia. (2014). Algoritma Klasifikasi Naive Bayes untuk Menilai Kelayakan Kredit. Universitas Dian Nuswantoro. Semarang.
[7] Islahulhaq. Wibowo. W.. & Ratih. I. D. (2021). Classification of Non-Performing Financing Using Logistic Regression and Synthetic Minority Over-sampling Technique-Nominal Continuous ( SMOTE-NC ). Int. J. Advance Soft Compu. Appl. 13(3): 115-128.
[8] Prasetyowati, Erwin. (2017). Data Mining: Pengelompokan Data untuk Informasi dan Evaluasi. Duta Media Publishing. Pamekasan.
[9] Sidiq. Y. N.. Fathonah. R. N.. & Riza. N. (2020). Metode Klasifikasi Menentukan Kenaikan Level UKM Bandung Timur Dengan Algoritma Naive Bayes pada Sistem JURAGAN Berbasis Komunitas. Kreatif Indonesia Nusantara. Bandung.
[10] Bahri, Syaiful & Lubis, Akhyar. (2020). Metode Klasifikasi Decision Tree untuk Memprediksi Juara English Premiere League. Jurnal Sintaksis. 2(1): 63-70.
[11] Nugraha, Jaka. (2014). Pengantar Analisis Data Kategorik. Deepublish. Yogyakarta.
[12] Faisal. M. R. & Nugrahadi. D. T. (2019). Belajar Data Science: Klasifikasi dengan Bahasa Pemrograman R. Scripta Cendekia. Banjarbaru.
Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.