Abstract
This study aims to find a financial distress prediction model with the highest accuracy. Types of Industries that is going to be selected is the basic & chemical industry: Metals, Ceramics, and Plastics in 2017-2020 which are listed on the Indonesia Stock Exchange. The determinant of the sample used is purposive sampling. The analysis was carried out using a financial distress prediction model and calculating the percentage of accuracy as well as type I error and type II error. The results of the analysis show that the condition of industrial companies increased in the Green Area and Red Area before decreasing in 2020 and decreasing in the Grey Area before an increase in 2020. The majority of observations are in distress in the Springate model, and the majority are in a healthy condition in Grover and Zmijewski. The Springate model is the model with the highest accuracy compared to other prediction models with the lowest I error rate of 21% and the second rank accuracy prediction result (56%).
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