Does Covid-19 Have an Impact on Bank Performance in Indonesia? A Comparative Analysis Based on BUKU
Bibliographic record
Abstract
This study aims to determine whether there are differences in the financial performance of commercial banks in Indonesia before and during the Covid-19 pandemic, with a major focus on capital, asset quality, profitability, and management efficiency based on BUKU (Bank Umum Kegiatan Usaha - Commercial Bank Business Activities). The data used in this study is secondary data, which consists of the 2015-2019 financial statements and the 1st quarter 2020 - the 3rd quarter 2020 financial statements. The sample used in this study amounted to 38 banks. The analytical method used is the Kruskal-Wallis test using the IBM SPSS version 25 software. The results of data processing and data analysis using the Kruskal-Wallis test show that there are differences in the capital (CAR), asset quality (NPL), profitability (ROA), and management efficiency (BOPO) of banking companies between BUKU 2, BUKU 3, and BUKU 4 before and during the covid-19 pandemic. The results of this study indicate that in general, the Covid-19 pandemic has an impact on the performance of commercial banks in Indonesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".