ANALYSIS OF FINANCIAL STATEMENTS BY COMPARING THE PERIOD BEFORE COVID 19 AND CURRENT COVID 19 TO MEASURE COMPANY PERFORMANCE IN PT ASTRA INTERNATIONAL TBK
Bibliographic record
Abstract
Financial reports can be used as a basis for assessing the performance of a company. So we need a form of financial statement analysis to determine the condition of the financial statements. Because the results of the analysis, the financial statements can be used for consideration by parties in determining the company's strategy. To get good results, financial analysis can be used by means of ratio analysis. The purpose of this study is to determine the performance of the company at PT Astra International Tbk in terms of financial reports using financial reports and ratio analysis as a benchmark. The analysis method used as a measurement of company performance is the ratio of solvency, activity, liquidity and profitability. The data and information studied were obtained from the IDX (Indonesia Stock Exchange). It can be concluded from each ratio as follows. From the solvency ratio it means that the company's capital is no longer sufficient to guarantee the debt given by creditors so that the condition of the company is said to be in a bad condition (insolvable). Judging by the activity ratio shows an increase every year so that the condition of the company is said to be in good condition. Based on the liquidity ratio each year has increased so that the company is categorized as in good condition (liquid). Based on the profitability ratio, it shows an increase from year to year so that it can be said that the company is in a good position. Keywords: analysis, financial reports, financial performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".