Implementasi Material Requirement Planning (MRP) untuk Pengelolaan Laboratorium Politeknik Industri Petrokimia Banten
Bibliographic record
Abstract
The availability of materials and equipment plays a crucial role in ensuring the continuity and smoothness of the learning process, especially in practical activities. However, in its implementation, there are still obstacles in the form of the unavailability of systematic instruments to support the decision-making process in purchasing practical materials. As a result, there is often a shortage or out-of-stock of practical materials during the activity, which ultimately hinders the learning process. To overcome this problem, this study applies the Material Requirement Planning (MRP) method as a tool in the process of planning practical material needs. MRP is used to analyze material needs based on the practical activity schedule and the availability of existing stock, so that it can be known more accurately when the right time is to reorder. By implementing the Material Requirement Planning (MRP) method, the decision-making process becomes more structured and efficient. This allows laboratory managers or responsible parties to order practical materials in a timely manner, so that supplies are maintained and practical activities can take place without obstacles
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".