Perencanaan Keuangan (RAPBS) Rencana Anggaran Pendapatan dan Belanja Sekolah dalam Pengembangan Sekolah di Smk Al Washliyah 9 Perbaungan
Bibliographic record
Abstract
The draft School Revenue and Expenditure Budget (RAPBS) is carried out through a process of managing school funds, of course involving various parties such as the school principal who regulates financial nets and their management is carried out by treasurers and other components that assist the process of implementing school finances. The purpose of this study was to analyze the implementation of financial planning (RAPBS) school income and expenditure budget plans in school development. This research was carried out with a qualitative approach using descriptive methods. Data collection techniques are observation, interviews and documentation as well as literature studies and group discussions. The results of this research are that the acceptance component of SMK Al Washliyah 9 Perbaungan is optimized by using educational funding sources, namely the existence of regular BOS funds sourced from APBN revenues. Reporting on the accountability of the RAPBS is carried out only to the extent of the school principal. And will be conveyed at the meeting with the parties involved in it. The principal submits a report, especially regarding school financial receipts and expenditures. Evaluation is carried out every quarter or per semester. The conclusion from this study is that good use of the RAPBS has made school development better, including: character education, human resource development and the use of technology. Keywords: RAPBS; Accountability; Development
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".