BNI Syariah Sebelum Pandemi Covid 19 Ditinjau Dari Prediksi Financial Distress
Bibliographic record
Abstract
In terms of the ratio of financing to third party funds, BNI Syariah has experienced a significant decline over the last 5 years. If this decline is not immediately resolved, it will be disrupted and the possibility of financial distress in banking can occur. The purpose of the study was to determine the financial distress experienced by BNI Syariah before the pandemic, namely 2016-2019. The research method used in this research is descriptive quantitative approach with calculations using modified Z-Score analysis. The results showed that in the last quarter of March 2016 to September 2018 before the COVID-19 pandemic, BNI Syariah was categorized as a gray area with a Z-Score value of 1.1 < Z" < 2.6, meaning that on this occasion it indicated that BNI Syariah was in the gray zone. gray area, so it cannot be ascertained whether the company is categorized as a healthy company or a company that is likely to go bankrupt, but in the quarter of December 2018 to June 2019 BNI Syariah is categorized as a distress zone, meaning that it indicates that the company has a high probability of going bankrupt.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".