Preferences of Acceptance for Gayo Arabica Coffee Based on Sensory Test Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
Bibliographic record
Abstract
Gayo Arabica coffee has more than one variety, and each variety has their own taste. The sensory test can be done to see the level of consumer acceptance for Gayo Arabica coffee based on several considered variables. Sensory evaluation through a fuzzy approach is expected to result in several data accumulations from various panelists, making it easier for decision-making. This research is purposed to develop an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, which can be used in predicting panelist acceptance levels towards various varieties of Gayo Arabica coffee products by using different membership functions. Attributes in the sensory test involved fragrance, acidity, body, aftertaste, and flavour as input variables, whereas consumer acceptance level was used as output variables. By 60% of the data were used as training data and 40% of the data were used as testing data with ANFIS model. The result of the research showed that ANFIS model with generalized bell and gaussian membership function has the lowest error value, which is 12.71% and 13.88%. That result indicates that ANFIS model with both membership functions is suitable to be used in estimating the acceptance level for Gayo Arabica coffee by consumers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".