Decision-Making System for Acceptance of Gayo Arabica Coffee Steeped Products with a Mixture of Herbs Using the MOORA Method
Bibliographic record
Abstract
Currently, processed coffee products with the addition of nutritious ingredients such as herbs are experiencing rapid development. Various herbal mixtures can produce delicious coffee, but consumer acceptance varies due to the sometimes-inconsistent brewing composition between coffee and the blending ingredients. Therefore, this research aims to determine the best decision-making system for accepting the Gayo Arabica coffee brewed product with a mixture of herbs using the MOORA method. There were 5 products analyzed, namely cuminpresso, olivepresso, kurmapresso, karipresso, and honeypresso. Furthermore, 30 ml espresso was used as the mixture base with the addition of 5 ml, 7 ml, and 10 ml of herbs for each product. The results showed that the panelists had different acceptance levels for each type of brewing. Therefore, the alternatives were obtained such that cuminpresso attained an acceptable level with the addition of 5 ml of black cumin oil, olivepresso with the addition of 5 ml of olive oil, kurmapresso with the addition of 10 ml of dates, karipresso with the addition of 5 ml of boiled water from curry leaves and honeypresso with the addition of 7 ml of honey.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".