PENERAPAN METODE MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS (MOORA) DALAM SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KADAR MINYAK MENTAH
Bibliographic record
Abstract
PT Perkebunan Nusantara IV Jambi Bah Plantation Business Unit is one of the large oil palm plantations that produce crude palm oil.The requirements for the quality of palm oil used as raw material for the food and non-food industries are different. Therefore authenticity, purity, freshness and other aspects must be paid more attention. The selection of crude palm oil levels carried out in the processing section is still manually. This requires quite a long time in the process and does not rule out the possibility of errors in his judgment. Based on the above assessment, there is a problem often found in determining high-quality and high-quality oil, which is to determine the appropriate and appropriate assessment in grading the content or aspects contained in crude oil according to standards and quality. Based on these problems, we need a system that is able to help solve problems in determining the quality of crude oil levels and quality. Thus it can be proposed operational development in determining the levels contained in crude oil using the MOORA method in deciding quality oil levels based on existing criteria. From the test results tested with 16 alternatives, the highest results can be obtained, namely alternative A3 with an alternative name 8774 LU with a value of 0.2738 so that it can be concluded that alternative A3 has high quality and quality oil content.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".