Management of Inorganic Fertilizer Raw Materials PT Citra Sawit Indah Lestasi using EOQ
Bibliographic record
Abstract
PT Citra Sawit Indah Lestari is a business operating in the oil palm plantation sector. Based on initial observations, it turns out that the supply of inorganic fertilizer raw materials at PT Citra Sawit Indah Lestari has not been planned properly so that one time the raw materials run out during the production process, it often happens that excess orders for inorganic fertilizer raw materials result in the raw materials not being able to be used. . And the storage warehouse is full, which will disrupt the operations of raw material collection by employees. The aim of this research is to apply Economic Order Quantity (EOQ) in controlling inorganic fertilizer raw materials at PT Citra Sawit Indah Lestari to maintain the stability of oil palm fruit production. The research method used in this research is qualitative research. The results of this research are that the system designed is in accordance with the needs of PT Citra Sawit Indah Lestari and makes work easier in controlling inorganic fertilizer raw materials. The conclusion is that the application of the Economic Order Quantity method in managing the supply of inorganic fertilizer raw materials at the web-based PT Citra Sawit Indah Lestari makes it easier for business owners to manage fertilizer supplies well so that it is easier to order goods in the next period.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".