| dc.description.abstract | This study aims to find out and analyze the comparison of five financial distress models (modified Altman Z-Score, Springate, Taffler, Zmijewski, and Grover) in identifying the financial zones of infrastructure sector companies listed on the Indonesia Stock Exchange for the 2018-2023 period. Then it aims to determine which prediction model has the highest accuracy level in predicting a company's potential bankruptcy and evaluate the type I and type II error rates of each model as an indicator of prediction reliability. The type of research used in this study is comparative research with a quantitative approach, using secondary data. The population in this study is 69 infrastructure sector companies listed as public companies (Tbk) on the Indonesia Stock Exchange for the 2018-2023 period. The sampling technique used in the study is the purposive sampling method, obtained from 43 infrastructure companies that will be the sample of this study, with the object of the research being the company's financial statements from 2018 to 2023 as many as 258 financial statements. The data analysis technique uses nonparametric statistics, through MS. Excel and the Kruskal-Wallis Test in SPSS 30. The results showed that: There was a statistically significant difference between the five prediction models (modified Altman Z-Score, Springate, Taffler, Zmijewski, and Grover) in predicting potential bankruptcy. The results obtained were Chi-Square 378.475 > 9.487729 and the significance level of Asymp—Sig (P-Value) < 0.05 which is 0.001. The model with the highest accuracy rate was the Springate model which obtained the highest accuracy level of 79.1%, compared to the modified Altman Z-Score model, Taffler, Zmijewski, and Grover and Type Error I of 20.9%. | en_US |
