Strategi Pengembangan Ekowisata Bale Mangrove Dalam Meningkatkan Jumlah Kunjungan Wisatawan Di Dusun Poton Bako, Jerowaru, Lombok Timur
Bibliographic record
Abstract
Mangrove ecotourism is one form of tourism that can be developed in Indonesia this research aims to find out what strategies, supporters and obstacles are applied and faced by mangrove bale ecotourism to develop the ecotourism to be better known by local, national and foreign tourists. This research uses Qualitative Research, which is research that produces descriptive data in the form of written or spoken words from people and behaviors that can be observed by taking a qualitative approach. The types of data used in this research are primary data and secondary data through observation, interviews and documentation, which can be obtained directly from pokdarwis, pokmaswas, tourists, reports and photographs. The data collection technique used is non-participant observation, which is an indirect researcher. From the results of the research conducted, it can be concluded that the mangrove bale ecotourism development strategy carried out by Pokdarwis and pokmaswas in increasing the number of tourist visits in Poton Bako hamlet, Jeroawaru as follows: (1) collaborating with the central government, vocational schools and universities as a channel to introduce mangrove bale ecotourism, (2) promoting mangrove bale ecotourism through social media such as Facebook, Instagram, Tiktok and Google my Business. In addition, there are supporting factors, namely support from the central government, unspoiled natural beauty and tour packages provided such as Explore Mangrove, Mangrove Camp, Mangrove Edutour, Explore Teluk Jukung and canoe racing while inhibiting factors are the lack of support from local and village governments and the lack of adequate human resources to support the development of mangrove bale ecotourism. Keywords: Development Strategy, Ecotourism, 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".