SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAHAN PERTANIAN YANG TEPAT UNTUK MENINGKATKAN HASIL PANEN CABAI MENGGUNAKAN METODE MOORA
Bibliographic record
Abstract
Decision support system is a system that can solve problems that occur in ranking quickly and can find out the highest to lowest value in a selection. In this paper is one of the case studies that can be solved using a decision support system, where the problem faced in the selection of agricultural land is how to choose the best chili and to make a selection must use manually and the assessment process takes a long time. to get results. Therefore created a problem how to determine an appropriate agricultural land selection problem to determine the good chili crop yield using the MOORA method and where the MOORA method is used to test in correctness that aims to determine the accuracy of the value obtained by the system, the results of the best land selection is in the city area of binjai jln sawi payaroba west binjai, plus the value of the weight of the criterion and the modification trial which aims to find out how many criteria can be added.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".