Factors Influencing Tourist Visit in North Sulawesi, Indonesia
Bibliographic record
Abstract
This study aims to determine the factors that influence the level of tourism visit on several tourism destinations in North Sulawesi Province. The research method used in this research is quantitative. Population in this study is people or tourist that visited any tourism attraction in North Sulawesi Province. The sample size of 100 respondents is the visitors of the tourism attraction in North Sulawesi Province. Data analysis using linear regression test using SPSS. The results showed that the characteristics of the respondents were generally male, namely 55%, the level of education, namely Senior High School with 68%, The respondents in age between 21-30 years old are 70 respondents or contributed 70%. Attraction factors, price, promotion, and security are significantly influence on tourist visit, and on the other hand, place doesn’t have a significant influence because place is closely related to the mileage. The result of this research can be a reference for Tourism Department of North Sulawesi considering the Promotion (X4) as the strongest variable that can improve the tourist visit level in North Sulawesi Province.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".