Classification of Company Performance Status Using Ensemble Learning and SMOTE by Considering CGI

  • Affindi Mario Bagaskara Institut Teknologi Sepuluh Nopember
  • Arif Djunaidy Institut Teknologi Sepuluh Nopember
  • Retno Aulia Vinarti
Keywords: Financial Distress Prediction, Multi-class classification, Ensemble learning, SMOTE, Corporate Governance Indicators


The global financial crisis that occurred in 2008 made most companies disturbed and unstable. In Indonesia, the impact of the global financial crisis can be felt continuously at the end of 2008. Indonesia's economic growth began from 2008-2009, the lowest percentage in 2009. This shows that the Indonesian economy is very important by the global financial crisis in 2009. Important for company to measure and know the status of the company's financial condition. A predictive model is needed to assist in analyzing the company's performance status for risk management. Therefore, building an effective Financial Distress Prediction (FDP) model has become an important research topic. This study aims to develop the FDP model by combining the ensemble learning method with the Synthetic Minority Oversampling Technique (SMOTE) method in state-owned companies in Indonesia listed on the Indonesia Stock Exchange. Then, in addition to using Financial Ratios (FR), this study also considers Corporate Governance Indicators (CGI). The experimental results of the FDP model development fell on the SMOTE-Stacking with an Accuracy result of 0.99 using FRs and CGIs data. This proves that the use of SMOTE and CGI methods is able to provide maximum prediction results. The result of this study is the FDP model which includes the company's performance status which is expected to increase the accuracy of the FDP model's performance.


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