IDENTIFIKASI PERILAKU, PERSEPSI, DAN MOTIVASI WISATAWAN BERKUNJUNG KE BANYUWANGI SERTA PENGARUHNYA TERHADAP PEMBERDAYAAN MASYARAKAT
Bibliographic record
Abstract
This study aims to analyze tourist behaviour, perceptions, motivation to visit, and satisfaction towards Banyuwangi tourism and its impact on community empowerment. This is a qualitative descriptive study using snowball sampling to collect research data. Research informants were the Culture & Tourism Office officer, an officer of the Cooperative, Medium Business, &Trade Office, 87 tourists, 4 guides, 11 micro-business actors, and 4 Local Tourism Awareness Group representatives. Results showed that most tourists visited Banyuwangi to see Ijen's blue fire, Alas Purwo, & local customs. They gained information from word of mouth, the Internet, & social media. Most tourists perceived that Banyuwangi tourism has good transportation facilities, supporting infrastructure, and accessibility with an affordable entry ticket. The primary motivations to visit Banyuwangi were to escape the routine, gathering with family & friends, increasing local culture knowledge, enjoying sports facilities, and adventure. Most tourists were satisfied & would like to revisit Banyuwangi & recommend it to others. In the last five years, Banyuwangi tourism shows significant developments. It has a positive effect on the community, especially for micro-business entrepreneurs. Keywords: community empowerment, motivation, perception, tourist behaviour
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".