Tourism Mix Factor: Tourists’ Travel Orientation in Choosing Types of Tourism Objects in Indonesia
Bibliographic record
Abstract
The purpose of this research is to analyze the tourism mix factor that differentiates tourists in choosing the type of tourism object as a basis for formulating strategies to attract tourists. This research is a quantitative study using a survey approach. The sample in this study was 200 respondents, consisting of 100 respondents of natural tourism visitors and 100 respondents of artificial tourism visitors. The analytical tool used in this study was descriptive analysis and discriminant analysis with the Stepwise method. The results of this study indicate that the variables that differentiate tourists from choosing natural tourism objects and artificial tourism objects are coolness, advertisements, facilities, prices, locations, travel agents, tourist attractions, transportation facilities and infrastructure, and public hospitality. Tourists who choose natural tourism objects have more positive attitude concerned with the variables of coolness, price, public hospitality, the existence of a travel agency, and the availability of transportation facilities and infrastructure, while visitors who choose artificial tourism objects have more positive attitude or are more concerned with location variables, advertisements, tourist attractions, and completeness of the facilities. This study provides a clear guidance on the tourism mix factor that distinguishes domestic tourists in Indonesia which is still very limited and need further investigation.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".