Policy of Disaster Mitigation and Post-Disaster Sustainable Tourism in Indonesia: Case Study of Tanjung Lesung Marine Tourism Banten
Bibliographic record
Abstract
Tanjung Lesung was a coastal tourist destination identified as a national strategic project for provincial economic development.It is located in Banten, Indonesia, and became a difficultto-revive tourist attraction after the tsunami disaster in 2018 and the COVID-19 pandemic.Strategic steps are needed to ensure optimum promotion and development of sustainable tourism policies.The research objects were the community around Tanjung Lesung, the Regional Government, and other stakeholders related to developing post-tsunami marine tourism in Tanjung Lesung.The method used was mixed-method research with quantitative and qualitative data analysis techniques.The study results showed a significant effect between tourism mitigation, tourism security, and sustainable tourism policies on marine tourism development in Tanjung Lesung.Furthermore, qualitative data show that the tsunami disaster and the COVID-19 pandemic have contributed negatively to developing marine tourism potential in Tanjung Lesung.Therefore, several efforts are needed to reduce these impacts.Tourism mitigation efforts need to minimize the impact of risks and improve the ability to adapt to disaster threats.Furthermore, a sense of security needs to be created for tourists while enjoying marine tourism destinations, and sustainable tourism policies need to be implemented to reduce the negative impacts of tourism.Therefore, several policy recommendations must be implemented: First, crisis management policies; Second, environmental and climate mitigation policies; Third, infrastructure and spatial planning policies; Fourth, sustainable tourism development policies; and Fifth, education and disaster response policies for tourism businesses, communities, and tourists.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".