Kontribusi Jumlah Pengunjung Obyek Wisata Dataran Tinggi Dieng Bagi Pendapatan Asli Daerah Kabupaten Wonosobo
Bibliographic record
Abstract
increasing regional revenue. For a region with limited potential of its’ natural resources it will be a challenge in an attempt to maximize the potential of the region. One of the effort to maximize the regional revenue is by optimizing potential in the tourism sector. Types of data in this research are secondary data such as tourist numbers, consumer price index, General Allocation Grant, and Local Revenue of Wonosobo Regency. The analytical tool is multiple regression analysis with statistical tests and classical assumption. This research aimed to understand the effect of the number of visits tourist, consumer price index, and General Allocation Grant against the Local Revenue of Wonosobo Regency from 2015 to 2017. The results of the regression processing of short-term models show that the consumer price index variable has a significant effect on Regional Original Income with a probability value of 0.0090 smaller than the real level α = 5%. While the variable number of visitors and General Allocation Funds did not have a significant effect on Regional Original Income with a probability value greater than the real level α = 5%.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".