Sustainable Tourism Development Strategy with AHP (Analytical Hierarchy Process) Method in Pagilaran Tea Plantation Agrotourism, Indonesia
Bibliographic record
Abstract
Tourism requires a strategy for development. The steps taken in developing tourism are quite complex. The tourism potential in the Pagilaran Tea Plantation can find a sustainable tourism model in an agro-tourism. Of course there are still many shortcomings from various aspects regarding the development of sustainable tourism in Pagilaran Tea Plantation Agrotourism, especially related to management. This study measures and examines the agro-tourism development strategy of Pagilaran Tea Plantation which has the advantage of being a sustainable tourism model with its social, economic, environmental and educational aspects. The right method used for this research is AHP (Analytical Hierarchy Process) because this method solves a complex unstructured situation into several components in a hierarchical arrangement, by assigning a subjective value to the relative importance of each variable, and determining which variable has the highest priority in order to influence the outcome of the situation. The results of the AHP analysis (Analytical Hierarchy Process) in obtaining priority from the criteria of factors in the sustainable tourism development strategy of Pagilaran Agrotourism, namely: Economic, Socio-Cultural, Ecological and Educational factors (0.351). The highest priority is education which gets a vector weight value (0.351). This result is in accordance with the vision and mission of the Pagilaran tea plantation company and the history of the Pagilaran tea plantation that the existence of this plantation is intended for education and research. Agrotourism is actually intended for educational tourism that provides insight and knowledge about agricultural commodities. Based on the results of the AHP analysis (Analytical Hierarchy Process) as a whole shows that the priority of alternative choices for sustainable tourism development strategies in Pagilaran Tea Plantation Agrotourism are as follows: 1st priority: Human Resources Development, 2nd priority: Tourism Infrastructure, 3rd priority: Tourism Marketing and 3rd priority: 4: Giving Capital.An alternative strategy that becomes a priority to be developed is human resource development (0.297). This is relevant to the development strategy of the Pagilaran tea plantation Agrotourism where the education aspect is a top priority.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".