Accuracy Analysis of the Financial Distress Prediction Model Using Altman Z-Score, Springate, Zmijewski And Grover in the Oil, Gas and Geothermal Mining Subsectors Listed on the Indonesian Stock Exchange (BEI) - Nurfadillah, Putri Sypa; Yulianti, Eka (SEAN Institute, 2024)
This study is to ascertain the accuracy of the financial distress prediction model in oil, gas, along with geothermal mining subsector firms for the 2018–2022 period by applying the Altman Z–Score, Springate, Zmijewski, also Grover models. The research methodology employed in this study is descriptive quantitative, utilizing secondary data taken from the financial accounts of the organization. Using a purposive sample approach, the study's sample consisted of 10 firms, whereas the population consisted of 16 companies. The analysis technique utilized is the degree of accuracy as well as type of error using Microsoft Excel 2019 software. The outcomes of this study show that the Grover model has the highest accuracy percentage of 76%, preceding the Altman Z-Score model 24%, Springate 22% as well as Zmijewski 60%.