PERHITUNGAN ANGGARAN BIAYA TENAGA KERJA LANGSUNG PADA PT. ANEKA SUMBER TATA BAHARI DI DESA TULEHU
Bibliographic record
Abstract
This research aims to find out how to prepare the direct labor cost budget calculation at PT. Various Sources of Maritime Management in Tulehu Village in January-March 2024. To find out the calculation of the direct labor cost budget at PT. The Sumber Tata Bahari annex requires production data at PT. ASTB and working hours data wage/hour rates. The method used in this research is a qualitative method. The results of the research can be seen from the preparation of the company's direct labor cost budget with theoretical studies that are appropriate, where, based on the calculation results, a direct labor cost budget for each employee can be obtained, fish washing production for each month of January is IDR 37,125,000, divided by 15 employees the result is IDR 2,475,000 per person, in February IDR 38,610,000 divided by 15 employees the result is amounting to IDR 2,574,000 per person, and whereas in March IDR 37,125,000 was divided by 15 employees the result is IDR 2,475,000 per person. So one person's income for 3 months is IDR 7,524,000, while the total wage during the first quarter is IDR 112,860,000. with results that have been calculated in accordance with company regulations with total wages for 15 employees amounting to IDR 112,860,000 for the 3 quarter periods January-March 2024 at PT. ASTB.
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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 | 0.001 |
| 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) | 0.048 | 0.010 |
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".