ANALYSIS OF BUDGET DISTRIBUTION IMBALANCE BETWEEN QUARTERS AT HIGH SCHOOL GOTONG ROYONG KOTA BANGUN: IMPACT ON FINANCIAL MANAGEMENT
Bibliographic record
Abstract
Effective budget management is essential for the smooth operation and development of an educational institution. This study aims to analyze the imbalance of budget distribution between quarters at SMA Gotong Royong Kota Bangun in fiscal year 2024, and evaluate its impact on financial management and school operations. Using a qualitative descriptive approach, data were collected through budget documents and interviews with financial managers and school principals. The results showed that there was a striking imbalance in budget distribution between quarters. The budget, which totaled IDR 72,128,000, was dominated by personnel expenditure (81.52%), with limited allocations for capital expenditure (4.62%) and facility maintenance (9.24%). Quarter III saw an unplanned surge in the budget, while Quarter II saw a significant decrease, especially in the allocation for capital expenditure and maintenance. Factors causing this imbalance include inadequate budget planning, limited funds at the beginning of the year, and sudden changes in needs. As a result, schools experienced operational disruptions, such as limited infrastructure development and minimal investment in technology. This budget imbalance also reduces the efficiency of school financial management. Based on these findings, it is recommended that SMA Gotong Royong Kota Bangun conduct more thorough and equitable budget planning between quarters, taking into account long-term needs and facility development. Increased allocations for capital expenditure and facility maintenance also need to be considered so that financial management is more efficient and supports the quality of education in a sustainable manner.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.004 | 0.000 |
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".