Analisis Loan to Deposite Ratio pada Bank Pemerintah dan Bank Swasta di Kalimantan Barat
Bibliographic record
Abstract
This study titled analyzes loan to deposite ratio (LDR) at government banks and private bank in west Kalimantan. The problem is how the development loan to deposite ratio in state bank and private bank in west Kalimantan. While the purpose of this study is to analyzes the ratio of loan to deposite ratio at government banks and private banks. In this study the author uses descriptive research mhetod is done by collecting nominal to get an overview of the reseach result. Approach is the method used in this research is quantitative method, A process of discovering knowledge in the form of figures that tracks that find information about what we want to know. The amount of data used was eight years old in each quarter. The result showed that the level of development of LDR on government banks and private banks based safe limits its loan to deposite ratio of a bank is about 85% - 100% or according Kasmir safe limits for LDR according to government regulations is a maximum of 100. Then the state banks have the highest LDR figure of 89,30% and the lowest value of 79,90%. While private banks are still under the safe LDR value occurs only once in 2012 to the IV quarter with 88,3% LDR figure, While the lowest figure 61,2%
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".