Sustainability Gastronomy Tourism in Medan City
Bibliographic record
Abstract
This research aims to find out how the current profile of tourism in Medan City is and how is the sustainable development gastronomic tourism in Medan City at this time. The method used by the researcher in this study is qualitative method with observation, interview method: The interviews that will be conducted in this study are interviews with key informants, namely the management of gastronomic tourism in the city of Medan and several stakeholders related to the development of culinary tourism in the city of Medan. Medan, literature study method, documentation method. The objects studied are gastronomic tourism products in the city of Medan the technique sample used convenience sampling. The data analysis technique used in this research is data triangulation. The research results show that the profile of the culinary tourism potential of the city of Medan is: (1) Bolu Meranti, (2) Merdeka Walk, (3) Ramadhan Fair, (4) Bika Ambon, (5) ie Aceh Titi Bobrok, (6) Ucok Durian, (7) Pagaruyung Culinary, (8) Tip Top Restaurant, (9) Ocean Pacific and (10) Amaliun Foodcourt (11) Kesawan Square. This study indicates that the potential for gastronomic tourism in Indonesia is Medan city in general, all of its gastronomic tourism has potential to develop. Meanwhile, the study of Gastronomic Tourism in Medan City is: (1) Developing a Strategy, (2) Sharpening brand power, (3) Promoting Medan Food and Creating the right Route for promotion. This research presents future research directions in the field of sustainable gastronomic tourism.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".