Determination of Priority for Recipients of Distribution Assistance Facility IKM Business Actors (Small and Medium Industries) Using the Moora Method in Langkat District
Bibliographic record
Abstract
To determine priority recipients of assistance facilities for IKM business actors in Langkat Regency, the criteria used in this study are the type of production, capital requirements, sales results, number of employees, and length of business. The data used in this study comes from the Department of Trade and Industry. In this study the MOORA method was used which aims to design and build a decision support system in determining priority recipients of facilities for IKM business actors. The author wants to make this decision support system using the Visual Basic application and supported by the MySQL Database so that the results are more effective and efficient. From the 10 sample data obtained by means of prioritizing recipients of assistance facilities for IKM business actors, it can be seen that those who get rank I with the highest score i.e. 0.1133 is A06 in the name of devi.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".