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Implementation of Random Forest Classifier for Financial Distress Prediction in Technology Sector Companies on the Indonesia Stock Exchange

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  • Netania Pangalinan

    (Universitas Negeri Makassar, Indonesia)

  • Muhammad Rakib

    (Universitas Negeri Makassar, Indonesia)

  • Muhammad Ashdaq

    (Universitas Negeri Makassar, Indonesia)

Abstract

Using the Random Forest Classifier algorithm, this study produces a financial crisis prediction model for technology companies listed on the Indonesia Stock Exchange. Historical financial statement data of eight technology companies were analyzed to generate financial indicators as prediction inputs. The results show that this model has an accuracy of 88%, with an increase in accuracy to 92% after resampling techniques. Important financial indicators used include liquidity, profitability, activity and leverage ratios. This research provides an effective predictive tool to identify companies at risk of financial distress, assisting financial management and investment decision-making.

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  • Netania Pangalinan & Muhammad Rakib & Muhammad Ashdaq, 2024. "Implementation of Random Forest Classifier for Financial Distress Prediction in Technology Sector Companies on the Indonesia Stock Exchange," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 9(8), pages 135-144, August.
    • Handle: RePEc:bjf:journl:v:9:y:2024:i:8:p:135-144
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