Analysis of Indonesian Islamic and Conventional Banking Before and After 2008
Bibliographic record
Abstract
This study aims to analyze the performance of Islamic banks and conventional banks before and after the implementation of Islamic Banking Act 2008. The performance will be measured using CAMEL ratio selected. This research is considered essential in examining the positive contribution of the application of the Act to improve the performance of Islamic banks in Indonesia. By using secondary data, this study compared the performance of Islamic banks with that conventional bank selected as samples during the study period. Data were analyzed using the Wilcoxon Signed Rank Test for inter-temporal and Mann-Whitney test for inter-bank. Inter-temporal Tests conducted on Islamic Banking showed that a significant difference was only seen in the NPF ratio of 2 years before and after implementation of Islamic Banking Act. As for conventional banks showed a more diverse ie for 1 year before and after the application of the Law on Islamic Banking there are significant differences for the ROA and ROE, two years before and after implementation of the Law Islamic banking there are significant differences for the CAR, ROA, ROE and NIM and for the overall test a significant difference to CAR, ROA, ROE, NIM and efficiency. Inter-bank testing showed that prior to the application of Islamic Banking Act there are significant differences between conventional banks and Islamic banks to CAR, ROA and efficiency. Furthermore, after the application of Islamic Banking Act there is a significant difference for the CAR and LDR / FDR.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".