GELIAT MEDIA SOSIAL DAN PENGARUHNYA TERHADAP MINAT BERKUNJUNG WISATAWAN
Bibliographic record
Abstract
The influence of social media on tourism has risen, since through it tourists can get information to help them in their travel planning process. This article will focus on looking at the influence of social media on the visiting interest of domestic tourists using indicators proposed by Taprial dan Kanwar (2012), with Nusa Penida as a case study. The research that forms the basis for writing this article was carried out in the fourth quarter of 2020 when the Covid-19 pandemic occurred, hence all the questionnaires were distributed online through Google Forms. The study found that while the variables of ‘accessibility’, ‘speed’, ‘interactivity’, ‘volatility’, and ‘range’ simultaneously have a significant effect, only ‘interactivity’ and ‘range’ have a partially significant positive effect on visiting interest of domestic tourists. Furthermore, although the regression model can statistically be used to determine the interest in visiting domestic tourists, the effect is not large, only 49%, while the remaining 51% is influenced by other factors. This indicates that a deeper research is needed on social media indicators that, directly or indirectly, affect the interest of domestic tourists to visit a particular tourist site such as Nusa Penida.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".