STRATEGI INTEGRASI PEMASARAN PADA DESTINASI WISATA CANDI SOJIWAN
Bibliographic record
Abstract
This research, actually has two major aims namely: (1) to identified potencies those are have in Sojiwan Temple Tourism Area; (2) to formulate integrated marketing strategic to enhance the number of tourist visits in Sojiwan Temple Tourism Area. To achieve the objectives, the research methods used qualitative methods. The research methods used is rasionalistic paradigm. The qualitative analysis used in this research are observation, in-depth interviews, and Focus Group Discussion. After data has collected, it will clarify with analyses needs. The findings of this research reveals: (1) that the Sojiwan Temple Tourism Area has a great potencies to develop, not only the Sojiwan Temple, but also it nature and cultural tourism; (2) integrated marketing strategic that sould be done in Sojiwan Temple Tourism Area are: determining target market, accomodating customer needs, and planning integrated marketing. Integrated marketing is very important to develop Sojiwan Temple Tourism Area, to enhance the number of visits, and to improve local revenue in Klaten District. Thus they have to having regards to the preservation because Sojiwan Temple Tourism Area is a heritage sites. Based on these findings, this research has some recommendation to the Klaten Government, the communities of Kebondalem Kidul, private sectors, and the other research. The development priority, preferable use tourism attraction development that educational basically and should be more directed towards community involvement in its management, because tourism is the industry that is involving public, private sectors, and community.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".