Analisis Perbandingan Profitabilitas Perusahaan Jasa Sebelum dan Selama Pandemi COVID-19
Bibliographic record
Abstract
This paper aims to find out how significant the comparison of company profitability is before and during the Covid-19 pandemic.The population in this study were all companies in the hospitality industry, restaurant and tourism sub-sector listed on the Indonesia Stock Exchange (BEI) for the period 2019 - 2020. The sample in this study used the purposive sampling method, namely companies that had complete financial reports for the 3rd quarter of 2019 and Quarter 3 of 2020, the number of samples of this study were 31 companies. The results prove that the Gross Profit Margin (GPM), Net Profit Margin (NPM), Operating Profit Margin (OPM), and Return On Asset (ROA) tested with the test wicoxon have a significant effect on the Covid-19 pandemic on GPM, NPM, OPM and ROA. The Return On Equity (ROE) analyzed using the mann whitney also experienced significant differences before and during the Covid-19 pandemic. That way there is a significant difference in company profitability as measured by GPM, NPM, OPM, ROA, and ROE, the hospitality industry, restaurants and tourism sub-sectors listed on the Indonesia Stock Exchange (IDX) before the Covid-19 pandemic and during the Covid-19 pandemic. It is hoped that this research can help investors and interested parties in responding to the Covid-19 pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".