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Financial Distress (FD) is a large concept consists several situations where the enterprise facing financial difficulty. Some manufacturing enterprise that are experiencing financial difficulties trying to overcome these problems by make loans and merger, or otherwise shut down their company. If the condition of FD is known, it is expected to take action to improve the situation so that the enterprise will not experience more severe difficulties such as bankruptcy or liquidation. The purpose of this study is to determine the factors that allegedly significantly influence for financial distress, and knowing classification model of financial distress. Thus, this study will try to classify the dynamic financial distress companies that listed on stock exchanges in Indonesia 2012-2014 using binary logistic regression and classification and regression tree analysis (CART). Before analyzing the binary logistic regression and CART, first the data must be balanced using SMOTE. Binary logistic regression developed the model of FD to see the factors that allegedly significantly influence for financial distress, while CART method is to solve the classification. Experimental results show that binary logistic regression has two predictor variables that significantly influence for FD condition for manufacturing enterprise and that variable is liquidity ratio and the activity ratio. CART classification method produces that maximum classification tree is equal to the optimum classification tree, with the primary node is variable solvency ratio, and the value of a classification. CART method outperforms the binary logistic regression with the classification’s accuracy has more than ten percent.

Classification of Dynamic Financial Distress Manufacturing Company Listed in Indonesia Stock Exchange Using Binary Logistic Regression and Classification Analysis Regression Tree