A Neuro-Fuzzy Approach for Financial Distress Prediction in the Basic Materials Sector Using Altman Z-Score

Financial distress becomes a serious threat to the sustainability of a company that has high volatility and encounters rapid market changes. The basic materials sector is one of the sectors that is vulnerable to financial distress risks due to the dynamics of commodity price fluctuations, global demand, and operational costs. In Indonesia, the basic materials sector plays a crucial economic role in the national industrial supply chain by providing essential needs for various industrial sectors. However, currently, companies in this sector are experiencing a decline in performance, with the index of this sector experiencing a sharper decline of 10.54% in April 2025, triggered by global sentiment that put pressure on the market. This indicates a decline in performance that leads to potential financial distress. This study proposes a hybrid prediction model that utilizes financial ratios from Altman Z-Score as feature-engineered inputs and ANFIS as a regression model through financial reports available on IDX. The results with the best parameters obtained an RMSE value of 0.00273 and an accuracy up to 100%. These findings confirm that the integration of statistical models and AI-based approaches not only improves classification performance but also maintains the interpretability of results for decision-making.