04-Penerapan Algoritma Logistic Regression Untuk Klasifikasi Risiko Kredit Pada Koperasi-Femulia Arifka Nanda

Koperasi Simpan Pinjam

Abstract

Cooperatives are important organizations or business units in improving the community's economy. The main activity of cooperatives is to provide loans to their members, but in the process of providing loans, cooperatives face several problems, one of which is inadequate debt. Bad debt causes various negative impacts on companies, such as a decline in company profits. These problems can be overcome through a systematic and data-based approach in credit risk classification. Credit risk classification can be performed using the Logistic Regression method. This study aims to determine the accuracy of the Logistic Regression method in credit risk classification. The implementation of the method used in this study consists of several stages: data collection, data preprocessing, data division, model training, testing, and evaluation. The results of the study show that the model built is capable of classifying credit risk with an accuracy rate of 90.91%, precision of 92.86%, recall of 92.86%, and F1-score of 92.86%. This indicates that the Logistic Regression method is effective and accurate for credit risk classification in savings and loan cooperatives.