PREFERENSI KONSUMEN TELUR PUYUH DI KOTA KENDARI
Bibliographic record
Abstract
The preference of quail egg’s consumer could be determined by measuring the consumer choice. This preference could illustrate the attribute of quail egg that could be produced by producer and accepted by consumer. The aims of this study were to analyze the attitude and behavior of the customer in case of buying quail egg, analyze the level of self interest and satisfaction of the customer to the quail egg attribute, and analyze the customer preference on a variety of the quail egg attributes. Data collected in Kendari, South East Sulawesi and were analyzed using Fishbein Multiattribute Attitude analysis, IPA analysis (Importance Performance Analysis), and Conjoint Analysis. The result of Fishbein Multiattribute Attitude Analysis indicated that the 61,91% consumers were fond of quail egg. In average, the acceptabiity of the customer to the quail egg was ‘quite good’ and the behavior of the customer was consistent but they to re-buy the quail egg. While the result of IPA analysis (Importance Performance Analysis) showed that the most important and the most satisfine attribute of quail egg for customer was colourful attribute. The result of Conjoint Analysis revealed that the preference of the quail egg customer in Kendari City was the quail egg with low price, seasonal availability, less than 10 gram in weight, colourful and unpacking. Keywords: quail egg; Fishbein; IPA; Conjoint analysis
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".