Marketing Development Strategy Of Milkfish Satay Business Using Analytical Hierarchy Process Method
Bibliographic record
Abstract
Marketing strategy is one of the spearheads of a healthy business. In practice marketing strategy faces a complicated problem since generally a good and standard strategy is only experienced by a business that has been running long and remains healthy so that it continues to generate profits. It is interesting to discuss exclusive food businesses (local products) that have a weakness of quick expiration date of only 1 day in delivery and 3 days in an open room (not in the packaging). It was found 4 P marketing strategy (Product, Price, Place & Promotion) so that the Analytical Hierarchy Process model was made. The results of the study showed that the top priority level for marketing in the product category was quality such as taste that was always the same and delicious. In addition, the other three criteria namely price, place, and promotion were at a non-negotiable normal price to give an exclusive impression, added facilities to provide a comfortable impression for consumers and maintain good public relations, for example, the strong relationship about satay milkfish product through word of mouth, the mutualism symbiosis relationship between local and national TV channels. The interesting thing in the choice of marketing strategies was offline marketing rather than online marketing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".