ANALISIS TINGKAT AKURASI MODEL PREDIKSI FINANCIAL DISTRESS MENGGUNAKAN METODE NAÏVE BAYES
DOI:
https://doi.org/10.61393/heiema.v5i2.525Keywords:
Financial Distress, Analisis Finansial, Data Mining, Naive BayesAbstract
Accurate prediction of financial distress is crucial, particularly for State-Owned Enterprises (SOEs), due to their strategic role in maintaining national economic stability. This study aims to analyze the accuracy of financial distress prediction models using Altman Z-Score and Zmijewski X-Score, in Indonesian State-Owned Enterprises during the 2021–2023 period. The research employed a quantitative comparative method using both financial ratios and financial statement account data of State-Owned Enterprises by being evaluated through the Naïve Bayes classifier method. The results reveal that the Altman Z-Score model achieved the highest level of accuracy, with 85% when predicted using financial ratios while Zmijewski X-Score achieved the highest level of accuracy with 57.5% when using financial statement accounts. These results suggest that the Altman Z-Score model provides superior predictive capability in identifying early signs of financial distress among Indonesian SOEs. Therefore, this model can be considered a more reliable analytical tool for stakeholders, policymakers, and financial analysts in assessing the financial sustainability and risk exposure of State-Owned Enterprises.
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