Sensory Analysis of Pliek U Using the Analytical Hierarchy Process (AHP) Method
Bibliographic record
Abstract
The people of Aceh usually use coconut flesh to produce three derivative products, namely Pliek u oil, Simplah oil, and Pliek u oil. Pliek u is made from coconut flesh (Cocos nucifera L.) that has been fermented, dried, and oil-extracted without the addition of microbes. It has developed into a traditional recipe for cooking spices, chili sauce, and salad. Therefore, this study aims to perform sensory analysis on various forms of Pliek u based on its thickness and fermentation time using the Analytical Hierarchy Process (AHP). The product was assessed for certain sensory parameters, such as color, aroma, and taste. According to the preference of the panelists for product acceptance, the sensory criteria with the highest priority weight were taste, aroma, and color at 0.550, 0.230, and 0.219, respectively. The result showed that the highest alternative weight was observed in the Pliek u with a thickness of 30 cm and a fermentation time of 7 days (K3F2) at 0.212, while a thickness of 10 cm and a fermentation time of 3 days (K1F1) were obtained the lowest alternative weight of 0.042. Also, the overall consistency value of Pliek u sensory taste was 0.02, which was acceptable since it was less than the pairwise consistency level of 0.1. According to the descriptive test, the best result was obtained by processing Pliek u at a thickness of 30 cm and a fermentation time of 7 days (K3F2), with a light brown color, no rancid odor, and a slightly sour taste.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".