Pelatihan Pemanfaatan Teknologi Drone dalam Pembuatan Video Promosi Wisata sebagai Optimalisasi Pengembangan Potensi Desa Wisata Kreatif Terong
Bibliographic record
Abstract
Desa Wisata Kreatif Terong is beginning to be recognized by all parties as one of the pioneering tourism villages that "dares" in showing its identity, daring to introduce its local wisdom and daring to carry out new innovations to support the growth of Belitung Island tourism. However, the development of the village faces various challenges, including a lack of public understanding of tourism management and minimal access to relevant training. One of the needs for tourism village managers that must be present in tourism management is the creation of content to promote their tourism, so training is needed in the use of drone technology as an effort to optimize the development of tourism village potential packaged in the form of community service activities. This activity was attended by 20 participants consisting of members of the Tourism Awareness Group of Terong Village and Keciput Village, Sijuk District, Belitung Regency. This activity has produced several outputs, namely successfully increasing the knowledge and skills of the community in operating drones, creating a local creative team ready to play a role in creating promotional content on an ongoing basis, and strengthening the collaborative network between universities and the village government. For the sustainability of the program, it is recommended that similar training activities be carried out periodically with a wider scope of material, such as professional video editing techniques, digital marketing, and tourism social media management
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".