ANALISIS Z-SCORE DALAM MENGUKUR KINERJA KEUANGAN UNTUK MEMPREDIKSI KEBANGKRUTAN PERUSAHAAN MANUFAKTUR PADA MASA PANDEMI COVID-19
Bibliographic record
Abstract
A manufacturing company is a business entity whose main activity is to process raw materials into finished goods, therefore they have a sale value. During the covid-19 pandemic, many manufacturing companies were threatened with bankruptcy. That is because the company’s performance has decreased. The purpose of this research is to compare how big the opportunities of PT. Astra International, PT. Mandom Indonesia, PT. Gudang Garam, and PT. Sri Rejeki Isman bankruptcy as a result of covid-19 by using the Altman z-score model. Financial distress is a situation where a company experiences liquidity difficulties or the ability to fulfill its obligations. Based on the results of PT. Astra International from 2016 to 2020 in the first quarter was potentially bankruptcy, while in the second quarter the company was based on the grey area. PT. Mandom Indonesia both in the first quarter and second quarter in healthy. PT. Gudang Garam in first quarter and second quarter in the grey area. PT. Sri Rejeki Isman in the first quarter and second quarter classified as a potentially bankrupt company.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".