Analysis Of Financial Distress Accuracy Level In The Textile And Garment Sector
Keywords:
Financial distress, Springate Model, Zmijezwki Model, Grover ModelAbstract
Introduction/Main Objectives: The Indonesian Textile and Garment Industry is currently experiencing a severe crisis characterized by mass layoffs and factory closures. This situation is driven by the influx of imported products and declining export demand, making financial distress prediction crucial for stakeholders. The objective of this study is to compare the accuracy of three prediction models—Springate, Zmijewski, and Grover—in detecting financial distress.
Background Problems: The crisis has caused widespread financial instability in the sector, raising the question: Which prediction model provides the most accurate early warning for financial distress in Textile and Garment companies listed on the Indonesia Stock Exchange (IDX)?
Research Methods: This research adopts a quantitative approach. The population includes all Textile and Garment companies listed on IDX, with 11 companies selected through purposive sampling, resulting in 44 data units. Data analysis techniques include descriptive statistics, the Shapiro-Wilk normality test, and One-Way ANOVA to compare the models’ predictive capabilities.
Finding/Results: The results indicate significant differences among the three models. The Zmijewski model achieved the highest accuracy at 100% with zero error, followed by the Grover model at 82% accuracy (18% error). The Springate model performed the worst, with only 9% accuracy and a 91% error rate.
Conclusion: The study concludes that the Zmijewski model is the most accurate and reliable tool for predicting financial distress in Indonesia’s Textile and Garment sector. Its adoption can help stakeholders implement timely interventions to mitigate risks and maintain business continuity.
References
annisa, R. A., & Ghozali, I. (2020). Pendeteksian Kecurangan Laporan Keuangan Menggunakan Analisis Beneish M-Score Pada Perusahaan Non Keuangan Yang Terdaftar Di Bursa Efek Indonesia Tahun 2017-2018. Diponegoro Journal Of Accouting, 9(3), 1–12. Http://Ejournal-S1.Undip.Ac.Id/Index.Php/Accounting
Bukhori, I., Kusumawati, R., & Meilani, M. (2022). Prediction Of Financial Distress In Manufacturing Companies: Evidence From Indonesia. Journal Of Accounting And Investment, 23(3), 588–605. Https://Doi.Org/10.18196/Jai.V23i3.15217
Elia, R., & Rahayu, Y. (2021). Analisis Prediksi Financial Distress Dengan Model Springate, Zmijewski, Dan Grover. Jurnal Ilmu Dan Riset Akuntansi, 10, 16.
Fahma, Y. T., & Setyaningsih, N. D. (2019). Analisis Financial Distress Dengan Metode Altman, Zmijewski, Grover, Springate, Ohlson Dan Zavgren Untuk Memprediksi Kebangkrutan Perusahaan Ritel. Ilmiah Bisnis Dan Ekonomi Asia, 15(2), 200–216. Https://Doi.Org/10.32812/Jibeka.V15i2.398
Fitriani, M., & Huda, N. (2020). Analisis Prediksi Financial Distress Dengan Metode Springate (S-Score) Pada Pt Garuda Indonesia Tbk. Nominal: Barometer Riset Akuntansi Dan Manajemen, 9(1), 45–62. Https://Doi.Org/10.21831/Nominal.V9i1.30352
Hayat, W. N. (2025). Raksasa Tekstil Di Bandung Pailit, Sudah Setop Operasi Sejak 2024. Cbnc Indonesia. Https://Www.Cnbcindonesia.Com/Market/20250929120928-17-671044/Raksasa-Tekstil-Di-Bandung-Pailit-Sudah-Setop-Operasi-Sejak-2024
Indriani, S., Ahmadi, & Wulansari, F. (2023). Analisis Financial Distress Pada Perusahaan Properti Yang Terdaftar Di Bursa Efek Indonesia (Bei) Dengan Metode Springate Periode 2020-2022. Armada, 5(2), 265–273.
Listyarini, F. (2020). Analisis Perbandingan Prediksi Kondisi Financial Distress Dengan Menggunakan Model Altman, Springate Dan Zmijewski. Jurnal Bina Akuntansi, 7(1), 1–20. Https://Doi.Org/10.52859/Jba.V7i1.71
Melissa, P., & Banjarnahor, H. (2020). Analisis Prediksi Kebangkrutan Menggunakan Model Altman Z-Score, Springate Dan Zmijewski Yang Terdaftar Di Bursa Efek Indonesia Analysis Of The Bankruptcy Prediction Using Altman Z-Score, Springate And Zmijewski Models Witch Is Listed In Bursa Efek Indone. Journal Emba, 8(1), 903–912.
Mufidah, K., & Handayani, A. (2024). Prediksi Financial Distress Pada Sektor Perbankan Dengan Menggunakan Metode Altman Z-Score, Grover, Springate Dan Zmijewski. Jurnal Ekonomi Manajemen Sistem Informasi, 5(6), 540–553. Https://Doi.Org/10.38035/Jemsi.V5i6.2479
Muslimin, D. W., & Bahri, S. (2023). Pengaruh Gcg, Ukuran Perusahaan, Dan Sales Growth Terhadap Financial Distress. Owner, 7(1), 293–301. Https://Doi.Org/10.33395/Owner.V7i1.1249
Oliviana, N., & Pandin, M. Y. R. (2023). Analisis Financial Distress Dengan Menggunakan Metode Altmn Z-Score Dan Springate S-Score Pada Perusahaan Sub Sektor Industri Makanan Dan Minuman Yang Terdaftar Di Bursa Efek Indonesia Tahun 2017 – 2021. Journal Of Student Research, 1(2), 334–346.
Panai, R. S., Hadady, H., & Bailusy, M. N. (2023). Analisis Perbandingan Ketepatan Prediksi Fi-Nancial Distress Perusahaan Menggunakan Metode Altman, Springate, Zmijewski, Dan …. Jimat (Jurnal Ilmiah …, 66–80. Https://Ejournal.Undiksha.Ac.Id/Index.Php/S1ak/Article/View/20543%0ahttps://Ejournal.Undiksha.Ac.Id/Index.Php/S1ak/Article/Download/20543/12526
Sriwahyuni, D., Azwari, C. A., & Rachmania. (2025). Analisis Perbandingan Tingkat Akurasi Model Altman Z-Score , Springate , Zmijewski , Taffler , Dan Grover Dalam Memprediksi Financial Distress Pada Bpd Yang Memiliki Uus Tahun 2019-2024. Jurnal Akuntansi Dan Bisnis, 10(2), 179–190.
Suadnyana, M., & Musmini, L. Sri. (2022). Analisis Financial Distress Pada Perusahaan Otomotif Yang Terdaftar Di Bursa Efek Indonesia. Jurnal Akuntansi Profesi, 33(1), 21–30. Https://Doi.Org/10.25299/Kiat.2022.Vol33(1).9907
Wahyuni, S. F., & Rubiyah. (2021). Wahyuni, S. F., & Rubiyah. (2021). Analisis Financial Distress Menggunakan Metode Altman Z-Score, Springate, Zmijeski Dan Grover Indonesia. Manieggio: Jurnal Ilmiah Magister Manajemen, 4(1), 62–72.
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