ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PROFITABILITAS PERBANKAN (STUDI PADA PT. BANK NEGARA INDONESIA (PERSERO), TBK, PERIODE 2010-2015)
Bibliographic record
Abstract
The financial condition of PT Bank Negara Indonesia (BNI) in each quarter of each year, the 2010-2015 period can be quite good. It can be seen from the growth in net profit generated by the BNI in each quarter of each year is always increasing. However, in the second quarter 2015 net income generated BNI only Rp. 2.46 Trillion. The net profit decreased compared to the net profit generated in the second quarter of 2014 amounted to Rp. 4.95 Trillion. In addition, ROA of BNI in the second quarter 2015 amounted to 1.14%. BNI ROA The ROA decreased compared with the second quarter of 2014 amounted to 2.43%. In this regard, it is necessary to do research on the factors that affect the profitability of BNI. The variables of this study is the Capital Adequacy Ratio (CAR), Non Performing Loan (NPL), Loan to Deposit Ratio (LDR), Operating Expenses Operating Income (ROA), and Net Interest Margin (NIM). As for profitability measurement tools using the Return On Asset (ROA). Source data extracted from BNI Quarterly Financial Statements First Quarter period 2010 to the third quarter of 2015. The data used is secondary data, which is a time series data. The analysis technique used is multiple linear regression. The results showed thatCAR, NPL, LDR, ROA and NIM simultaneously have significant effect on ROA. Partially, CAR, NPL, and LDR have not significant effect on ROA. BOPO have significant negative effect on ROA and NIM have significant positive effect on ROA. The magnitude of the influence CAR, NPL, LDR, ROA and NIM to ROA amounted to 94.3%, while the rest 5.7% is explained by other variables outside the model. Keywords : Capital Adequacy Ratio, Non Performing Loan, Loan to Deposit Ratio , Biaya Operasional Pendapatan Operasional, Net Interest Margin, Return On Asset
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".