ARAHAN PENGEMBANGAN JALUR WISATA BERDASARKAN ANALISIS LINKAGE SYSTEM DI KABUPATEN NGADA
Bibliographic record
Abstract
The contribution of the tourism sector in the regency is based on gross regional domestic product in 2014- 2016 the average increased by 6,65 % per year. The purpose of this research is to identify the potential of tourism objects with qualitative descriptive, analilyze the linkage system between tourism object to identify the derection of tourism development with scoring techniques. Data collection techniques include observation of documentation and interviews. The results showed that the attractiveness of tourism objects in the regency of Ngada varied from natural scenery, legacy of megalithic origin. Supporting facilities are only available for hotels, restaurants and souvenir shops and the 17th Alam Island Tourism Park and the Mangeruda hot spring. Accessibility of tourism objects in the form of asphalt , can be passed by 2 wheeled vehicles and 4 wheeld vehicles. Promotion of tourism objects is carried out by cultural and tourism services through instagram. Backward linkage to the availability of 2 travel agents in the city of Bajawa. Forward linkage in the form of mileage from Bajawa city to the furthest tourism object is 17 Riung island with a distance of 75 km and closest is Lekolodo Ecotourism with a distance of 6 km. The tourism management system is carried out by cuture and tourism office in collaboration with BKSDA and the village community. Directions for tourism development based on scoring analysis of tourism potential in the form of tourist routes I distance of 45 km, tourism route II distance 32 km, tourist III 57 km.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".