| This study aims to build logit model prediction of financial distress and which can be used to predict the condition of the mining industry in Indonesia. Logit model of financial distress prediction is constructed using indicators of financial distress, namely Economic Value Added (EVA) as the dependent variable of the study. As for the independent variables, researchers used the model variables Altaian and financial ratios used to assess the performance of the company. These variables are XI = Net Working Capital to Total Assets, X2 = Retained Earnings to Total Assets, X3 = EBIT to Total Assets, X4 = Book Value of Equity to Book Value of Liabilities, X5 = Current Ratio, X6 = Long Term Debt to Total Assets, X7 = Cash Flow From Operations to Total Equity and X8 = Cash Flow From Operations to Total Liabilities. Object of research in the form of a company engaged in the mining industry sector in Indonesia and listed on the Indonesia Stock Exchange (listed) during the period 2009 to 2013 amounted to 24 companies. This study uses statistical analysis techniques stepwise binary logistic regression method with SPSS. Results from this study indicate that the model predictive classification model of EVA has a power of 78.3%. At mdoel these studies obtained three (3) independent variables are statistically significant effect on the Net Working Capital to Total Assets, Earnings Before Interest and Taxes to Total Assets and Cash Flow From Operations to Total Liabilities. |