Analysis of the Determining Factors of Financial Distress (A Case Study at PT. Bank Rakyat Indonesia (Persero)
Bibliographic record
Abstract
Economic misery is a circumstance in which the debtor can not satisfy his/her duties to creditors after they fall due. economic distress is the country of the company experiencing economic problems and is threatened with financial ruin. The motive of this study become to decide the impact of Capital Adequacy Ratio (vehicle), Operational costs on working profits (BOPO), Non appearing Loans (NPL), loan to Deposite Ratio (LDR), and go back On belongings (ROA) on monetary distress. This studies changed into conducted at PT (restrained enterprise) bank Rakyat Indonesia (Persero) Tbk. The number of samples is 32 with purposive sampling method. information became gathered using a documentation method, specifically through quarterly monetary document data from 2013-2020 posted at the monetary offerings Authority internet site, www.o.k.go.identity. The effects of speculation testing suggest that the Capital Adequacy Ratio (vehicle) variable has a positive impact on economic misery. Operational fees on running profits (BOPO) have a nice impact on economic distress. Non-appearing loan (NPL) has a terrible effect on financial misery. mortgage to deposit ratio (LDR) has a terrible impact on monetary distress. return on belongings (ROA) has a high quality impact on economic distress. therefore, Capital Adequacy Ratio (automobile), Operational price to operating profits (BOPO), Non acting mortgage (NPL), loan to Deposit Ratio (LDR), and return On assets (ROA) simultaneously affect economic distress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".