Prediksi Finansial Distress pada Salah Satu Bank Konvensional Menggunakan Machine Learning

Abstract

Financial distress is when a company experiences a shortage or insufficient funds to run the company. Prediction of financial distress is needed to prevent bankruptcy. In this study, financial distress predictions were made based on financial ratios obtained from monthly financial reports from a bank convention, after which the proportion that had the most influence on financial distress was determined. The models used in this study are several machine learning models, namely, Logistic Regression, Support Vector Machine, and Random Forest. Based on the analysis results, the best model for predicting financial pressure is the Random Forest Model, with an accuracy of 96.77%. Based on the best model obtained, namely the Random Forest, it can be determined that the ratio that is very influential on financial distress is the ratio of Total Asset Turnover.