ANALISIS POTENSI FINANCIAL DISTRESS DENGAN MENGGUNAKAN ALTMAN Z SCORE PADA PERUSAHAN PENERBANGAN (DAMPAK PANDEMI COVID-19 DENGAN PENUTUPAN OBJEK WISATA DAN PSBB)
Bibliographic record
Abstract
This study aims to identify and analyze the potential for financial distress in airlines at Indonesia. The object of this research is the airlines listed on the Indonesia Stock Exchange (BEI), namely PT. Garuda Indonesia Tbk and PT. AirAsia Indonesia Tbk. The data used is in the form of financial reports that have been published on the Indonesia Stock Exchange through the website (www.idx.co.id) in the first quarter of 2020 – third quarter of 2020. The data analysis technique uses the Altman Z Score in predicting potential financial distress. The results of the study found that PT Garuda Indonesia Tbk and PT AirAsia Indonesia Tbk were in financial distress or in an unhealthy financial condition, and were classified as companies that have the potential to experience bankruptcy. Research shows that PT AirAsia Indonesia Tbk has a higher potential for bankruptcy than PT Garuda Indonesia Tbk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".