ANALISIS EFISIENSI BANK BUMN DENGAN MENGGUNAKAN PENDEKATAN STOCHASTIC FRONTIER ANALYSIS (SFA) PERIODE 2019-2021
Bibliographic record
Abstract
The purpose of the study was to determine the level of efficiency of state-owned banks for the 2019-2021 period. This research is a descriptive study with a quantitative approach that uses secondary data from the Financial Statements of State-owned Banks for the 2019-2021 period on the research object of PT. Bank Negara Indonesia Tbk., PT. Bank Rakyat Indonesia Tbk., PT. State Savings Bank Tbk., and PT. Bank Mandiri Tbk., obtained from the website of the Financial Services Authority. The data processed is data for the quarter of 2019 to the third quarter of 2021. The efficiency calculation is carried out using the Parametric Aprroach Stochastic Frontier Analysis which is processed using STATA 17. \nThe results of the study using calculations show that state-owned banks in the 2019-2021 period have an average efficiency level of 0.9955 or close to 1. The results of these calculations indicate that state-owned banks are efficient in managing inputs into optimal output. State-owned banks that have the highest efficiency scores are Bank BRI and Bank Mandiri, followed by Bank BNI, then Bank BTN. State-owned banks still need to supervise managerially and evaluate the allocation of deposits to productive assets in order to obtain greater profits, greater credit distribution and improve capital buffers so that bank efficiency can improve the overall economy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".