Analisis Prediksi Potensi Kebangkrutan Pt Indo Asia Sukses Dengan Model Zmijewski Dan Model Springate
Bibliographic record
Abstract
This research aims to analyze potential bankruptcies of PT Indo Asia Sukses using Zmijewski and Springate method in period of 2016-2018. The data used is Primary data coming from the company. This research uses qualitative method with two data collections, those are documentation and theoritical study method. The result of research shows that: (1) According to Zmijewski X-Score, the company was in good condition with the negative result in 2016-2018. (2) According to Springate S-Score, the company was in good condition in 2016-2018, but the first quarter of 2017 showed the company had potential to get bankrupt. According to the result of research, it hoped that the company could improve the sales beacuse the research shows that low in sales could get the company bankrupt in first quarter of 2017 and the company could gain more awareness of financial if similar condition happening in the future.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".