PERENCANAAN STRUKTUR PERKERASAN LANDAS PACU BANDAR UDARA SYAMSUDIN NOOR – BANJARMASIN
Bibliographic record
Abstract
Bandar U dara Syamsudin Noor – Banjarmasin merupakan salah satu bandar udara yang dikelola PT. (Persero) Angkasa Pura I dan memiliki permintaan angkutan udara untuk penumpang dan kargo yang cukup potensial. Bandara Syamsudin Noor memiliki panjang landas pacu sebesar 2.500 x 45 m dengan arah azimuth 10 – 28. Metode perencanaan perkerasan struktural pada landas pacu bandar udara yang umum digunakan adalah metode CBR, metode FAA, metode LCN dari Inggris, metode Asphalt Institute dan metode Canadian Departement Of Transportation. Adapun tujuan dari penulisan tugas akhir ini adalah untuk me rencan ak an tebal perkerasan lentur pada landas pacu Bandar Udara Syamsudin Noor – Banjarmasin sepanjang 2500 m untuk pesawat rencana B 7 3 7- 900ER dengan menggunakan metode CBR ( US. Army Corps Of Engineers Design Method ) , metode FAA (Federal Aviation Administration) dan metode LCN (Load Classification Number), serta menganalisa kelebihan dan kekurangan masing-masing metode yang digunakan. Berdasarkan hasil perencanaan dari metode-metode perencanaan struktur perkerasan lentur yang digunakan diperoleh bahwa metode CBR ( US. Army Corps Of Engineers Design Method ) dan FAA (Federal Aviation Administration) memiliki tebal yang sama besar, yaitu sebesar 27 inchi atau 69cm, sedangkan untuk metode LCN (Load Classification Number) memiliki tebal paling besar, yaitu sebesar 38 inchi atau 97 cm. Hasil perencanaan tebal perkerasan dengan menggunakan metode CBR dan FAA sama dengan hasil perencanaan PT. (Persero) Angkasa Pura I dengan jenis lapis keras lentur (flexible pavement) sebesar 690 mm atau sama dengan 69 cm. Adapun material yang digunakan dalam perencanaan perkerasan lentur runway tersebut adalah : untuk lapisan surface digunakan Asphalt Concrete (AC), untuk base course digunakan material batu pecah, dan untuk subbase course digunakan material agregat alam.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".