Indonesian Journal of Applied Mathematics
Vol 3 No 1 (2023): Indonesian Journal of Applied Mathematics Vol. 3 No. 1 July Chapter

Prediksi Finansial Distress pada Salah Satu Bank Konvensional Menggunakan Machine Learning

Fuji Lestari (Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera)

Article Info

Publish Date
30 Jul 2023

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.

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Journal Info
Indonesian Journal of Applied Mathematics
Website

Abbrev

indojam

Publisher

Institut Teknologi Sumatera

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Physics

Description

Indonesian Journal of Applied Mathematics is a scientific publication media that publishes articles from the results of research or studies in the field of applied mathematics, focusing on Computational Mathematics, Optimization, Actuarial, Statistics, Numerical Modelling, Mathematical Physics, ...