Bibliographic record
Abstract
The purpose of this study is to analyze the soundness of banking in Indonesia before and during the pandemic using RGEC, which consists of Non Performing Loans (NPL), Good Corporate Governance (GCG), Return on Assets (ROA), and Capital Adequacy Ratio (CAR). The impact of this research is to assist banking companies in analyzing their level of health before and during the pandemic. This research is a quantitative study with a descriptive approach, using secondary data from the first quarter to the fourth quarter of 2019 (before the pandemic) and the first quarter to the fourth quarter of 2020 (during the pandemic). Based on data from banking companies listed on the Indonesia Stock Exchange, the population studied was 45 companies, with a sample of 35 companies. The results showed that there was no significant difference in the soundness of banks using the RGEC method as proxied by NPL, GCG, ROA, and CAR.
 Keywords: Banking Health; NPL; GCG; ROA; CAR
 
 
 Tujuan penelitian ini adalah menganalisis tingkat kesehatan perbankan di Indonesia sebelum dan saat pandemi menggunakan RGEC, yang terdiri dari Non-Performing Loan (NPL), Good Corporate Governance (GCG), Return on Assets (ROA), dan Capital Adequacy Ratio (CAR). Dampak dari penelitian ini adalah membantu perusahaan perbankan dalam menganalisis tingkat kesehatannya pada sebelum dan saat pandemi. Penelitian ini merupakan penelitian kuantitatif dengan pendekatan deskriptif, menggunakan data sekunder dari triwulan I hingga triwulan IV tahun 2019 (sebelum pandemi) dan triwulan I hingga triwulan IV 2020 (saat pandemi). Berdasarkan data perusahaan perbankan yang terdaftar di Bursa Efek Indonesia, populasi yang diteliti adalah 45 perusahaan, dengan sampel 35 perusahaan. Hasil penelitian menunjukkan bahwa tidak terdapat perbedaan signifikan pada tingkat kesehatan bank menggunakan metode RGEC yang diproksi dengan NPL, GCG, ROA dan CAR.
 Kata Kunci: Tingkat Kesehatan Bank; NPL; GCG; ROA; CAR
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".