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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".