Sektor Industri Pariwisata Dengan Media Digital Di Masa Pandemi Covid-19 Luh Suryatni
Bibliographic record
Abstract
Since the pandemic caused by the SARS-CoV-2 virus (COVID-19) appeared in the end of 2019 it significantly affected the tourism industry. Government in Indonesia have adopted emergency measures and restrictions that have affected the mobility of everyone. From museum to amusement park, they were empty, even closed since people’s movements were stopped, and travel among different territories was strictly controlled. Lockdown and quarantine around the world caused by COVID-19 has harmed people’s livelihoods and the world economy. During this situation government pushing the idea of the advantage of digital era and adopted by tourism industry. The purpose of this paper is to find out and analyzing tourism industry has facing during the pandemic and how it used digital technology in purpose of revive their condition. The data collection method used is a literature study with descriptive analysis techniques. The results are showed that Indonesia tourism industry starting to get better in situation by using digital media and working together with application that provides help for tourist planning the trip and open the new chances of new business.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".