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All Journal El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Journal of Ekonomics, Finance, and Management Studies
Sahlan, H.
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Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences

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Analisis Komparatif Model Prediksi Financial Distress pada Perusahaan Media di Bursa Efek Indonesia Muhlison, Muhlison; Sahlan, H.; Setiawan, Kurnia Ari
El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Vol. 6 No. 12 (2025): El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmal.v6i12.10073

Abstract

The digital disruption era has posed significant challenges to the sustainability of media companies in Indonesia, particularly through increasing risks of financial distress caused by shifting consumer behavior and fluctuating revenue streams. This study aims to analyze and compare the accuracy of three financial distress prediction models—Modified Altman Z Score, Zmijewski X Score, and Grover G Score—in detecting potential bankruptcy among media companies listed on the Indonesia Stock Exchange during the 2021–2023 period. A mixed methods approach was employed, combining quantitative analysis using descriptive and comparative techniques on financial statement data with qualitative evaluation of each model’s formula structure, empirical validation, and practical applicability. The results reveal that both the Altman and Grover models achieved the highest accuracy rate of 57.14%, while the Zmijewski model showed a lower accuracy of 42.86% with the highest Type I Error rate. Based on qualitative assessment, the Modified Altman Z Score is considered the most appropriate model for predicting financial distress in Indonesia’s media industry, given its strong theoretical foundation, broad empirical validation, and ease of application. These findings provide practical implications for investors, analysts, and corporate management in anticipating financial risks at an early stage and serve as a basis for developing advanced prediction models using artificial intelligence to enhance accuracy and adaptability in the future.
Analisis Komparatif Model Prediksi Financial Distress pada Perusahaan Media di Bursa Efek Indonesia Muhlison, Muhlison; Sahlan, H.; Setiawan, Kurnia Ari
El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Vol. 6 No. 12 (2025): El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmal.v6i12.10073

Abstract

The digital disruption era has posed significant challenges to the sustainability of media companies in Indonesia, particularly through increasing risks of financial distress caused by shifting consumer behavior and fluctuating revenue streams. This study aims to analyze and compare the accuracy of three financial distress prediction models—Modified Altman Z Score, Zmijewski X Score, and Grover G Score—in detecting potential bankruptcy among media companies listed on the Indonesia Stock Exchange during the 2021–2023 period. A mixed methods approach was employed, combining quantitative analysis using descriptive and comparative techniques on financial statement data with qualitative evaluation of each model’s formula structure, empirical validation, and practical applicability. The results reveal that both the Altman and Grover models achieved the highest accuracy rate of 57.14%, while the Zmijewski model showed a lower accuracy of 42.86% with the highest Type I Error rate. Based on qualitative assessment, the Modified Altman Z Score is considered the most appropriate model for predicting financial distress in Indonesia’s media industry, given its strong theoretical foundation, broad empirical validation, and ease of application. These findings provide practical implications for investors, analysts, and corporate management in anticipating financial risks at an early stage and serve as a basis for developing advanced prediction models using artificial intelligence to enhance accuracy and adaptability in the future.
Co-Authors Muhlison, Muhlison Setiawan, Kurnia Ari