HYGUITA, EZA RINE (2023) MODEL PREDIKSI KEBANGKRUTAN PERUSAHAAN MANUFAKTUR PERIODE 2018-2021. Masters thesis, UNIVERSITAS MA CHUNG.
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Abstract
Companies with good financial performance in managing assets will be able to generate high profitability. If the company is unable to anticipate and prepare for financial difficulties, then its business will decline and lead to bankruptcy. Bankruptcy is a serious problem, so an early warning system is needed that can detect the early potential for bankruptcy, so management will be greatly assisted.
This study aims to test whether there is a difference between bankruptcy prediction methods and which prediction method is the most accurate. The bankruptcy analysis methods used in this study include Altman z-score, Springate, Zmijewski, Grover, and Ohlson. Companies listed on the Indonesia Stock Exchange belonging to the manufacturing industry which receive a special notation are the samples in
this study. The research period used is 2018-2021. Based on purposive sampling obtained 5 companies that meet the sample criteria. Testing the hypothesis in this study was carried out using a different test, namely the Kruskal Wallis and accuracy test. The results of hypothesis testing related to the different test show that there are differences in the Altman z-score, Springate, Zmijewski, Grover, and Ohlson
bankruptcy prediction methods. In addition, the test results related to the accuracy test show that the Zmijewski method is the most accurate bankruptcy prediction
model among other models. How to deal with financial distress in companies can be done by analyzing debt and cash flow, and identifying areas that need
improvement. Keywords: analysis, bankruptcy prediction model, manufacturing
| Item Type: | Thesis (Masters) |
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| Additional Information: | TUGAS AKHIR |
| Subjects: | H Social Sciences > HJ Public Finance |
| Divisions: | Fakultas Ekonomi dan Bisnis > S1 Akuntansi |
| Depositing User: | Surya |
| Date Deposited: | 30 Oct 2024 03:57 |
| Last Modified: | 30 Oct 2024 03:57 |
| URI: | http://repository.machung.ac.id/id/eprint/318 |
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