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Hasanah, Silvi Uswatul
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Computer Science & IT

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ANALYSIS OF BANKRUPTCY PREDICTION IN HEALTH SECTOR COMPANIES LISTED ON THE INDONESIAN STOCK EXCHANGE IN THE PERIOD 2021-2023 Hasanah, Silvi Uswatul; Novius, Andri
International Journal of Business and Information Technology Vol. 6 No. 2 (2025): December
Publisher : LPPM STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/ijobit.v6i2.1359

Abstract

This study aims to analyze the bankruptcy of healthcare companies listed on the Indonesia Stock Exchange for the 2021-2023 period using the Altman Z-Score, Springate, Zmijewski, Grover, and Fulmer models and to determine whether there are differences in conditions from the results of the financial distress model analysis using the Altman Z-Score, Springate, Zmijewski, Grover, and Fulmer models and to determine the model that has the highest level of accuracy in predicting the potential for bankruptcy in companies. This research is a study with a mix method approach. The data source in this study is secondary data in the form of financial reports. The population in this study were healthcare companies listed on the Indonesia Stock Exchange for the 2021-2023 period. Sampling was carried out using a purposive sampling technique so that a sample of 23 companies and 96 analysis units were obtained. The results of this study indicate that Indofarma is the only company predicted to go bankrupt in the results of each financial distress model. The results of this study also show that there are differences in conditions from the results of the financial distress model analysis in healthcare companies. The model that has the highest level of accuracy in healthcare companies is the Grover model at 95.66%. The results of the best predictions and models can provide information in the form of positive signals and early warning systems for investors
Co-Authors Andri Novius