Analisis Perbandingan Model Prediksi Kebangkrutan Altman Z-Score, Springate, Grover, dan Zmijewski pada Perusahaan E-Commerce di Indonesia
DOI:
https://doi.org/10.35870/jemsi.v12i4.7010Keywords:
Bankruptcy Prediction, Financial Distress, Bankruptcy Models, Digital Companies, E-commerce, Indonesia Stock ExchangeAbstract
This study aims to evaluate the relevance of traditional bankruptcy prediction models in the context of digital companies, specifically within Indonesia's e-commerce sector. Although the e-commerce sector has experienced rapid growth, many companies in this sector continue to record sustained losses, raising questions about the accuracy of conventional bankruptcy models in assessing the financial condition of digital firms. This research employs a quantitative approach by analyzing the financial statements of three e-commerce companies listed on the Indonesia Stock Exchange: PT Bukalapak.com Tbk, PT Global Digital Niaga Tbk, and PT GoTo Gojek Tokopedia Tbk, covering the period from 2021 to 2024. The analysis utilizes four bankruptcy prediction models—Altman Z-Score, Springate, Grover, and Zmijewski—and includes the Kruskal–Wallis test to examine differences in outcomes among the models. The results indicate variations in predictions across models, suggesting differences in sensitivity when evaluating the financial condition of e-commerce companies. Furthermore, most models tend to classify these companies as being in financial distress, even though, operationally, they are still in a growth phase. These findings highlight the limitations of traditional bankruptcy prediction models in interpreting the financial characteristics of digital firms. The study implies that the application of bankruptcy prediction models should consider industry context, particularly for technology-based companies with growth patterns and financial structures that differ from those of conventional firms.
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References
Abadi, M., & Misidawati. (2023). Penggunaan rasio keuangan sebagai indikator kesehatan finansial dan prediksi kebangkrutan.
Abadi, M., & Misidawati. (2023). Prediksi kebangkrutan perusahaan. CV Literasi Nusantara Abadi.
Altman, E. I. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x.
Brigham, E. F., & Houston, J. F. (2018). Fundamentals of financial management (15th ed.). Cengage Learning.
Chrisnawan, & Norita. (2017). Analisis prediksi kebangkrutan menggunakan metode Altman, Grover, dan Fulmer.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
Damodaran, A. (2010). The dark side of valuation: Valuing young, distressed, and complex businesses. Wiley.
Effendi. (2018). Analisis prediksi kebangkrutan dengan metode Altman, Springate, Zmijewski, dan Grover.
Fitri Nurhasanah, S., Mulyani, S., & Kusuma, P. M. (2025). Analisis kebangkrutan perusahaan menggunakan metode Zmijewski dan metode Springate (Studi kasus pada perusahaan Food & Beverage yang terdaftar di BEI).
Grice, J. S., & Dugan, M. T. (2001). The limitations of bankruptcy prediction models. Review of Quantitative Finance and Accounting, 17(4), 321–340. https://doi.org/10.1023/A:1017973604789.
Hadityo, P., & Indrawati, E. (2024). Prediksi financial distress dengan model Altman Z-Score, Zmijewski, Springate, dan Grover di perusahaan department store. Journal of Management Research and Knowledge, 10(2), 55–73.
Kasmir. (2019). Analisis laporan keuangan (Edisi revisi). Rajawali Pers.
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441.
Masdiantini, & Warasniasih. (2020). Laporan keuangan dan prediksi kebangkrutan perusahaan.
Meilia, R. (2025). Integrated financial ratio analysis and bankruptcy forecasting in Indonesia’s e-commerce sector: Evidence from PT Gojek Tbk. Southeast Asian Journal of Management, 8(1), 15–29.
Mikha Novalina Sinaga, dkk. (2019). Analisis tingkat kebangkrutan pada perusahaan asuransi di BEI.
Nadila Regita Marindatu, Samsinar, & Nur Afiah. (2025). Analisis kebangkrutan dengan menggunakan metode Z-Score (Altman) pada perusahaan rokok yang terdaftar di Bursa Efek Indonesia.
Nurhayati, F., & Komara, L. (2025). Predictive analysis of financial distress using Altman Z-Score on trade, service & investment sector companies listed on the IDX (2019–2023). Journal of Financial Research, 14(3), 101–118.
Pandita, S., & Hasanah, R. (2024). Analisis financial distress dengan metode Zavgren dan Grover untuk memprediksi kebangkrutan PT Bukalapak.com Tbk. Indonesian Journal of Finance and Accounting, 5(2), 33–50.
Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress. Journal of Financial Service Professionals, 56(5), 48–57.
Pricilia Claudia Pangkey, dkk. (2018). Analisis prediksi kebangkrutan dengan metode Altman dan Zmijewski.
Purwadinata, S., & Batilmurik, R. W. (2020). Pengantar ilmu ekonomi: Kajian teoritis dan praktis mengatasi masalah pokok perekonomian. CV Literasi Nusantara Abadi.
Sawitri, N. N. (2025). Pengantar manajemen keuangan: Konsep dasar dan implementasi. Alifba Media.
Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm (MBA thesis). Simon Fraser University.
Syamsuddin, N., Dewantara, R., Pramana, I. B. B. S. A., Maryati, D. E., Sitanggang, M. S. H., Sinaga, H. D. E., Maldin, S. A., Dahliani, L., Fatmawati, & Sebayang, Y. B. (2022). Metode penelitian kuantitatif. Mataram: Sanabil.
Udin, A., & Puspitaningrum, D. (2025). Bankruptcy prediction of e-commerce companies on IDX using Altman Z-Score, Springate, and Zmijewski. Journal of Business and Technology, 12(2), 45–62.
Wahyuni, S. (2019). Pengantar ekonomi Islam. Samarinda: RV Pustaka Horizon.
Wisnu Haryo Prakoso, dkk. (2022). Analisis perbandingan model Taffler, Springate, dan Grover.
Yusnita Yunus, dkk. (2020). Analisis tingkat kebangkrutan PT Gaji Indonesia (Persero) Tbk.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(2), 59–82. https://doi.org/10.2307/2490859.
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