RISIKO PERBANKAN DENGAN ALTMAN Z SCORE : KAJIAN PADA BANK YANG TERDAFTAR DI BURSA EFEK INDONESIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. This research was conducted to find out banking risk with Altman Z Score on conventional banks listed on Indonesia Stock Exchange. The data used in this study is secondary data obtained from the annual financial statements of conventional banks during the period 2003-2016 contained in the official website of the Indonesia Stock Exchange.The focus of research (research focuses) on the topic that has been studied is the risk of banking based on Altman Z Score. Of the four studies examined, the equation is all research using quantitative research approach. It means to assess the Altman Z-Score in observing banking risk, the quantitative approach is the most appropriate approach.Of the four studies, the results show banking risk with Altman Z Score, from 2003-2016 the banks listed on the Indonesia Stock Exchange are generally in the category of bankrupt. However, Ganesha et al (2012) study shows the Z value model in 2003-2006 can not show a good enough accuracy level when measured per year. Irwansyah's research (2017) shows that in the period 2013-2016, only one bank, namely Bank Jtrust Indonesia Tbk (BCIC bank code) entered into the healthy category. In addition, Bank Mandiri (Persero) Tbk with BMRI bank code, has started to increase from the predicted category of bankruptcy to the prediction of gray area category.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it