This purpose of this study is to examine the relationship between managerial overconfidence and the prediction of bankruptcy and likelihood of the auditor's hesitation of the company's going concern over the financially distressed companies listed on the Indonesian Stock Exchange during 2014-2016. Managerial overconfidence is defined as the manager's excessive confidence that can be measured when the growth of the company's assets is higher than its sales growth. The financial condition is measured by the prediction of bankruptcy using the adaptive neuro-fuzzy inference system (ANFIS) model. The results showed that the likelihood of an auditor's hesitation of the company's going concerns is positively associated with managerial overconfidence in Indonesia because the auditor has a positive view on the ability of management. In addition, there is the likelihood that the auditor's hesitation of a company's going concerns is positively associated with the prediction of bankruptcy.

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Foreign Exchange (FOREX) is the trading of one cash against another. FOREX rates are affected by multitudinous affiliated plutocrat-related, political and internal factors and along these lines awaiting it may be a worrisome errand. The individualities included within the field of universal fiscal trade have looked for interpretations of rate changes and latterly, trusting to ameliorate vaticination capabilities. It's this capacity to directly prevision FOREX rate changes that allow for the maximization of profit. Trading at the correct time with fairly correct procedures can bring huge benefits, but an exchange grounded on off-base development can risk big mischances. numerous styles to prognosticate the FOREX rate consolidate quantifiable examination, time arrangement examination, featherlight systems, brain associations, and mix systems. These styles involve the sick impacts of the issue of directly anticipating the exchange. A Perceptroning presents information and predicts results that regard certain situations of unpredictability or randomness, and over inheritable Algorithm Learning Machine are proposed to prognosticate the longer-term pace of the FOREX show since can combine top and technical FOREX Information for Fundamental and Technical Analysis. The free factors considered in this consideration were the trade rates of China, Japan, Europe, Gold and Unrefined Oil to dissect the Rupiah trade rate inferior variable. For the examination, USDIDR is switching scale from the forex stamp. The Combination Stochastic and inheritable Algorithm Learning Machine Model fulfilled a MSE of 0.01 and a MAE of0.0082 during the preparation and testing stage.

This research is an application to realize a system that is capable of providing data and information between levels of economic growth and Indonesian export between 2010 and 2015. Data from the system that was created dug deeper to find out the prediction of drug distribution in the future. The system to be built is the system that is able to predict the level of export needs that will happen in time (month/year) that you want based on the data of the time (month/year) using ANFIS system. The ANFIS system will search the best function to predict the export needs in the year 2010. Furthermore, the output is used as the data in 2010. The data is output as the prediction will be matched with actual data, whether the resulting function of ANFIS system has a small error. If so, then the function obtained is optimal.