ANALISIS PERENCANAAN DAN PENGENDALIAN BIAYA PEMELIHARAAN AKTIVA TETAP (MESIN) UNTUK MENJAGA KELANCARAN PRODUKSI PADA PT. TROPICA COCOPRIMA
Bibliographic record
Abstract
This researchwas case studyon PT. Tropica Cocoprima, the title is “Analysis of Planning and Contolling Maintenance Cost of Fixed Assets (Mechanical) To Maintain Smooth Production”. PT. Tropica Cocoprima is a company that produces flour as the end product of the production process. In the production process, production machinery plays an important rolein providing products there for eurgently needed care in order to avoid frequent damage. This purpose of research is to analysis routin cost of eengine maintenance cost for planning and control at PT. Tropica Cocoprima. To plan and controlling cost , need to the holding of separation between variable cost and fixed cost. This research used the least squares method for separate the variable cost and fixed cost. Calculations with using analysis three difference, was found that the companies getting difference inprofitable for costs maintenance of machine fixed namely on difference efficiency because the unit that produced is 1.750.000 kg greater than the planned unit is 1.166.415 kg.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".