Sustainable Coastal Tourism: A Comprehensive Development Strategies (Tanjung Bira and Lemo-lemo Tourism Area as a Case Study)
Bibliographic record
Abstract
Tourism development has become an effective way to improve the economy and welfare of local communities in many areas, especially areas with high tourism potential.This research aims to formulate a tourism management strategy using SWOT Analysis and provide recommendations based on sustainable coastal tourism in the Tanjung Bira and Lemo-lemo tourism areas.The data collection method in this research uses primary and secondary data.Primary data was obtained by participatory observation and interviews.Secondary data was obtained by document review, which collected information related to policies, history, journals, and literature related to tourism.Data analysis was conducted using SWOT analysis, descriptive-qualitative, and comparative study to determine strengths, weaknesses, opportunities, and threats in formulating tourism strategies.Moreover, descriptive-qualitative analysis is used to formulate policies related to strategy based on sustainable coastal tourism.Based on an analysis of 35 internal and external factors, the coastal tourism development strategy can be carried out with the S-O Strategy (Integration between tourist locations, increasing the role of government and fulfilling vegetation), W-O Strategy (Improving facilities and infrastructure, Community-government cooperation, tourism promotion).S-T strategy (Improvement of regulations, accessibility, and community empowerment), W-T Strategy (Arrangement and direction of planning tourist 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".