PREDIKSI FINANCIAL DISTRESS MENGGUNAKAN ARTIFICIAL NEURAL NETWORK (ANN) PADA PERUSAHAAN PERBANKAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) PERIODE 2017-2021

Resky Awalia; Farida Titik Kristanti

doi:10.23969/oikos.v7i2.7924

Authors

Resky Awalia Telkom University
Farida Titik Kristanti Telkom University

DOI:

https://doi.org/10.23969/oikos.v7i2.7924

Abstract

The Earnings Per Share (EPS) value from 2017 to 2021 shows how banking companies listed on the Indonesia Stock Exchange (IDX) did. Several banking companies experienced a decrease in EPS values from 2017 to 2021 and even obtained a negative EPS value which could indicate that the company identified as having a poor profit growth value and even triggering financial distress. This study plans to foresee the event of monetary trouble in financial organizations recorded on the Indonesia Stock Trade for the 2017-2021 period by utilizing a Fake Brain Organization. The info boundaries utilized are monetary proportions, specifically the ongoing proportion, return on resources, obligation to-resource proportion, and complete resource turnover. The results of the study show that the four ratios are suitable for use as input parameters because they provide significant differences between companies that declared distress and non-distress. This study's prediction process utilized an ANN architecture consisting of 20 neurons as the input layer, 5 neurons as the hidden layer, and 1 neuron as the output layer, achieving the highest accuracy of 87 percent. Keywords: Artificial Neural Network, Financial Distress, Financial Ratios.

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