PREDIKSI FINANCIAL DISTRESS MENGGUNAKAN MODEL NEURO FUZZY DAN RASIO ALTMAN - MUKSAN JUNAIDI; Ratna Dwi Rahayu (Universitas Mercu Buana, 2020)
Financial distress is a condition of decline in corporate finance before business failure. This failure is closely related to insolvency, where the company has failed in carrying out its operations for profit. Financial distress prediction methods have long been developed by researchers in the field of accounting and finance using Multiple Discriminant Analyst (MDA). A common method is that the Altman model is used to predict financial distress one year in the future, this is as an early warning of the company's financial condition.The purpose of this study is to develop a data mining application neuro fuzzy algorithm model using the Altman model input data ratio to predict the company's financial distress for the next few years optimally. The data used are financial statements from 45 LQ45 Issuers on the Indonesia Stock Exchange (IDX) in 2013-2017. The initial process of calculating the balance sheet and profit (loss) financial statements through the Altman model ratio to get zscore values of three categories (safe, gray and distress). The values are sorted by four backward periods as input data to the model. Functional tools from the Matlab GUI program will be used for the formation, training and testing of the neuro fuzzy structure of the ANFIS 4MFs model. Furthermore, the predicted value of the model is compared with the value of the calculation of the target Altman ratio. The final results show that the ANFIS 4MFs model can provide highly optimal predictive values close to the average target ratio value of 100% in the membership functions of Gauss, Trapezoid, and G-bell.