DAMPAK PANDEMI COVID-19 TERHADAP KINERJA KEUANGAN DI PERUSAHAAN PERBANKAN
Bibliographic record
Abstract
The country's unstable economic condition due to the COVID-19 pandemic has a major impact on economic growth. The condition of the company is experiencing liquidity pressure which can result in default. This study is a quantitative study to determine the effect of financial performance in banking companies. This study uses a sample of commercial banks listed on the Indonesia Stock Exchange (IDX) before the COVID-19 pandemic occurred in the 2019 quarter period and the COVID-19 pandemic period in the 2020 quarter. The sampling technique used purposive sampling with certain criteria. The analysis technique using regression analysis was carried out with the classical assumption test. Based on the results of the analysis found that TPF has a positive and significant effect on Return on Assets (ROA), NPL has a negative and significant effect on Return on Assets (ROA), LDR has a positive effect on Return on Assets (ROA), CAR has a positive effect on Return on Assets ( ROA), and NIM have a positive and significant effect on Return on Assets (ROA). The contribution of this research is to determine the impact of the COVID-19 pandemic on the banking industry, which serves as an intermediary function between recipients and distributors of fundsKeyword : CAR, DPK, NIM, NPL, Covid-19 Pandemic, ROA
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".