<![CDATA[Business competition that is very tight in the current era of globalization, requires companies to be able to maintain their performance. Companies that are not able to compete are feared to experience financial distress until it leads to bankruptcy. Considering the importance of predicting financial distress for company managers, investors, creditors and decision-making institutions, it is necessary to make a prediction model for financial distress. Financial distress prediction model that was first developed by Altman, using the statistical method, Discriminant Analysis. In its use, Discriminant Analysis has the disadvantage of having to fulfill certain assumptions related to the predictor measurement scale, the relationship between predictors and distribution along with predictors. In this research a prediction model of financial distress will be made using a new method, Multivariate Adaptive Regression Spline (MARS). MARS was chosen because this method was developed using a nonparametric approach, so it does not require certain assumptions and is more flexible. The MARS method and Discriminant Analysis will be used to make predictive models of financial distress in manufacturing companies listed on the Indonesia Stock Exchange. The modeling resulting from the MARS and Discriminant Analysis methods will then be compared to the accuracy of the predictions. The results of this study indicate that the MARS model has a smaller classification error and is statistically consistent than the Discriminant Analysis method.]]>
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