DETERMINAN MARJIN PERBANKAN DI INDONESIA (Studi pada Bank Umum yang terdaftar dalam Bursa Efek Indonesia Periode Tahun 2012-2017)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui determinan marjin perbankan di Indonesia. Adapun variabel dependen penelitian adalah marjin bank, sedangkan variabel independen penelitian fokus kepada faktor internal perusahaan diantaranya adalah biaya operasional, efisiensi operasional, risiko kredit, risiko likuiditas, risk aversion, dan ukuran bank. Data penelitian merupakan data panel yaitu gabungan antara data cross section dengan sampel penelitian sebanyak 39 perusahaan perbankan yang terdaftar dalam Bursa Efek Indonesia (BEI) dan data time series yaitu pengambilan data penelitian dilakukan selama 6 (enam) tahun yaitu tahun 2012-2017. Pengujian hipotesis dilakukan menggunakan analisis regresi linier berganda, yang sebelumnya telah dilakukan terlebih dahulu pengujian asumsi klasik. Hasil pengujian menunjukkan bahwa biaya operasional berpengaruh positif terhadap marjin bank, efisiensi operasional yang ditunjukkan melalui rasio BOPO berpengaruh negatif terhadap marjin bank, risiko kredit yang ditunjukkan melalui rasio Non Performing Loan (NPL) tidak berpengaruh terhadap marjin bank, risiko likuiditas yang ditunjukkan melalui rasio Loan Deposit Ratio (LDR) berpengaruh positif terhadap marjin bank, risk aversion ditunjukkan melalui rasio Capital Adequacy Ratio (CAR) berpengaruh positif terhadap marjin bank, dan ukuran bank merupakan logaritma total kredit berpengaruh positif terhadap marjin bank.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".