Critical Factors for Increasing Tourism Competitiveness: Analysis of Travel and Tourism Competitiveness Index in Sumedang Regency
Bibliographic record
Abstract
The disruption of the tourism sector needs to be measured to increase tourism competitiveness. This study aims to map tourism competitiveness with the Travel and Tourism Competitiveness Index (TTCI) pillar after the Covid-19 pandemic. Of the 14 pillars of TTCI released by the World Economic Forum, only eight pillars with 56 indicators are used that are relevant to the condition and availability of data at the village level. This research uses a mixed method with a sequential explanatory design approach. The data uses village potential data from the Central Statistics Agency in 2018, 2019, and 2021. The analysis unit consists of 270 villages and seven sub-districts in Sumedang Regency. The results of a composite assessment of all TTCI indicators and pillars show a striking difference in the environmental sustainability pillar, which has increased very highly compared to before the Covid-19 pandemic. The results of the TTCI scoring have mapped aspects of potential, advantages, and weaknesses in terms of eight pillars of tourism competitiveness at the village, subdistrict, and district levels. This study presents actual data on the potential of villages to increase tourism competitiveness using TTCI parameters. It can be a reference for stakeholders to improve areas that are priorities for tourism development, especially facing the aftermath of the Covid-19 pandemic. The declining pillar of human resources during the pandemic requires the attention of stakeholders, especially in developing tourism priority areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".