DUKUNGAN MODA TRANSPORTASI UDARA DALAM PENGEMBANGAN KAWASAN INDUSTRI BULI KABUPATEN HALMAHERA TIMUR
Bibliographic record
Abstract
District East Halmahera in North Maluku, is district now become one of industrial estatewhich was developed by Ministry of Industry. A Plan to make as one of Industrial area andthis thing also had an influence on the performance of the air transportation, in the Buliairport on East Halmahera which currently categorized as class iii airport with kind of aircraftoperating of ATR 72-600 so that is considered need to be improved the ability of its service inorder to statisfy the demand for which it will follow and also have economic growth andregional development. The purpose of this research to know the number of passengers who willuse the Buli Airport in the year 2032, know the number of the frequency of flight in the year plansuch as in 2032. And knowing the dimension of the airside facilities include the runway, taxiway,and apron in accordance with plan of the Boing 737-500. From the data obtained we could predictat a later date how can the forecasting and demand that will happen. From the data can wereanalysed by using three (3) method of analysis as Linear trend, Kuadratis, and Exponensial. Andfrom the result of the analysis taken the best trend based on the Sum Of Square Error (SSE) toestimate the number of passenger as many as 50548 soul. While obtained the number of planes asmany as 861 a plane with the movement of as much as two the movement of daily. The result pointsto the need to design the addition of runway being 2600m, taxiway 152 x 15 m, and sidening of anapron 105 x 67 m. The result of design inclosed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".