Implementasi Metode Analytical Hierarchy Process Dan Interpolasi Linier Dalam Penentuan Lokasi Wisata Di Kabupaten Karangasem
Bibliographic record
Abstract
Bali is known for its tourism sector, so it has always been one of the alternative tourist destinations for local and foreign tourists. Almost every district in Bali has interesting tourist attractions to visit for tourists. When traveling, tourists usually decide to visit interesting tourist destinations. The number of tourist destinations available, often makes tourists confused about choosing a destination according to their preferences. Therefore, this research is intended for tourists to be able to determine alternative priority tourist sites in Karangasem Regency. In this study, data were collected from 75 respondents to find out alternative tourist sites in Karangasem, and to determine the criteria to be considered for traveling. These criteria are rides provided at tourist sites (C1), price of admission to tourist sites (C2), distance from tourist sites to city center (C3) and facilities provided at tourist sites (C4). 4 alternative tourism data used in the calculation by producing alternative tourist sites at Taman Ujung as the best alternative. The method used is the Analytical Hierarchy Process (AHP) to produce the weighted criteria, scoring the ticket price and distance values using Linear Interpolation and calculating the final value using the Cost and Benefit normalization process. The results of this study can provide alternative tourist locations for domestic tourists who want to vacation in Karangasem Regency.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".