Comparison of Financial Distress Prediction Model Accuracy Using Support Vector Machines and Discriminant Analysis Methods - Herlina; Mundari, Siti (Faculty of Engineering, Universitas Serambi Mekkah, 2025)
Financial distress is a stage before the company goes bankrupt. For this reason, the ability to predict financial can be useful information for companies and investors. This information is useful for companies to be able to improve their financial condition so that the company does not go bankrupt. For investors, this information is useful to avoid investor losses in capital investment. Studies on financial distress have been conducted for a long time, starting with using statistics until now being developed using artificial intelligence methods. The purpose of this study was to compare the accuracy of the financial distress prediction model for publicly traded manufacturing companies in miscellaneous industry sectors listed on the Indonesia Stock Exchange using the data mining method, namely Support Vector Machines, which is one of artificial intelligence method and the statistical method, namely Discriminant Analysis. From the research results, the two methods provide equally good accuracy. Based on the processed data, the accuracy of the two methods is 100%.