CASA, NIM, dan Profitabilitas Perbankan di Indonesia
Bibliographic record
Abstract
ABSTRACT This study aims to increase the role of CASA and NIM in improving the profitability of banks in Indonesia. This research was motivated by various CASA improvement strategies undertaken by banks to maximize profits, as well as OJK policies to support NIM to improve the efficiency and competitiveness of Indonesian banks. On the other hand, this research is also supported by the limited research that analyzes the relationship of CASA with banking profitability. The hypothesis in this study discusses using multiple linear regression. BEI in 2016-2018, this study proves that CASA and NIM are proven to increase bank profitability. These results prove that the proportion of CASA owned by banks can reduce the cost of funds resulting in increased profitability. This study also proves the ability of banks to generate profits from interest can support increased bank profitability. Additional analysis shows that CASA can increase NIM. Furthermore, NIM has also been proven to mediate CASA's relationship with banking profitability. Keywords: CASA, NIM, profitability, banking ABSTRAK Penelitian ini bertujuan untuk mengidentifikasi peran CASA dan NIM pada peningkatan profitabilitas perbankan di Indonesia. Penelitian ini dimotivasi oleh berbagai strategi peningkatan CASA yang dilakukan perbankan untuk memaksimalkan profit, serta kebijakan OJK untuk menekan NIM guna meningkatkan efisiensi dan daya saing perbankan Indonesia. Di sisi lain, penelitian ini juga didorong oleh masih terbatasnya penelitian yang menganalisis hubungan CASA dengan profitabilitas perbankan. Hipotesis dalam penelitian ini diuji dengan menggunakan regresi linier berganda. Dengan melakukan pengamatan pada perbankan yang menerbitkan sahamnya di BEI pada tahun 2016-2018, penelitian ini menunjukkan bahwa CASA dan NIM terbukti meningkatkan profitabilitas perbankan. Hasil ini membuktikan bahwa proporsi CASA yang dimiliki perbankan dapat menurunkan biaya dana sehingga terjadi kenaikan profitabilitas. Penelitian ini juga membuktikan bahwa kemampuan perbankan dalam menghasilkan laba dari bunga dapat mendorong kenaikan profitabilitas perbankan. Analisis tambahan menunjukkan bahwa CASA dapat meningkatkan NIM. Selanjutnya, NIM juga terbukti memediasi hubungan CASA dengan profitabilitas perbankan. Kata kunci: CASA, NIM, profitabilitas, perbankan
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".