Analisis Faktor-Faktor Yang Memengaruhi Kredit Macet pada Perbankan di Indonesia (Studi Kasus pada Kredit UMKM Q1 2017 − Q4 2019)
Bibliographic record
Abstract
Lending is the main activity of banks in generating profits, but the biggest risks are also derived from lending. According to the financial services authority of Indonesia statistics, based on the type of use, the most credit distribution is micro small and medium enterprises (MSMEs). It can be concluded that credit is the main target of lending by banks. This research was conducted to analyze how internal bank factors can affect bad MSMEs credit Indonesian banks in the first quarter of 2017 to the fourth quarter of 2019 with a CAMEL analysis approach. Independent variables used are Capital, Assets, Management, Earning, and Liquidity, each of which is proxied using CAR, KAP, NIM, ROA, and LDR ratios. The dependent variable used is bad credit, which is proxied by the NPL ratio. The data panel method is used to quantitatively estimate the parameters in the model. The results showed that banking capital fluctuated during 12 quarters, namely from 2017 to 2019. In terms of liquidity, it is known that some banks have liquidity ratios above the tolerance limit set by Bank Indonesia. It is necessary to have control of bank management to inhibit the aggressiveness of lending, as well as to raise deposit rates for banking funding to increase.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".