Penentuan Koperasi Terbaik pada Dinas Koperasi dan UMKM Kota Binjai menggunakan Metode WASPAS
Bibliographic record
Abstract
A cooperative is a business entity based on the principle of kinship. Cooperatives have the aim of improving the welfare of their members through the activities they carry out. The Binjai City Cooperatives and UMKM Service is a regional apparatus within the Binjai City Government for government affairs in the field of Cooperatives, Small and Medium Enterprises. The Binjai City Cooperatives and UMKM Department always carries out assessments of every cooperative in the city of Binjai in the form of an assessment of the best cooperatives which aims to increase the motivation of cooperative institutions and as an evaluation material for the performance of cooperatives recorded in the City of Binjai. In carrying out the assessment of the best cooperatives carried out by the Binjai City Cooperatives and UMKM Service, it took quite a long time. This is because the data collection and processing process is conventional and simple. To overcome existing problems, a decision support system was created to facilitate the management and calculations of each cooperative. In this research, the method used in the calculation process is WASPAS (Weighted Aggregated Sum Product Assessment). The Weighted Aggregated Sum Product Assessment (WASPAS) method is a method that is able to minimize errors or maximize the assessment to determine the highest and lowest values. The final result of this research is a decision support system that is able to produce decision recommendations in determining the best cooperative at the Binjai City Cooperative and UMKM Department.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".