STUDI KINERJA RUNWAY 3 DAN PENGARUH ADANYA CROSSING TAXIWAY TERHADAP KAPASITAS RUNWAY 2 DI BANDARA INTERNASIONAL SOEKARNO-HATTA
Bibliographic record
Abstract
Bandara Internasional Soekarno-Hatta merupakan bandara terpadat di Indonesia yang terus mengalami peningkatan jumlah penumpang. Sehingga diperlukan kapasitas runway yang lebih besar akibat pergerakan pesawat yang meningkat. Saat ini, bandara internasional Soekarno-Hatta memiliki runway 3 terbaru sepanjang 3000 x 60 meter untuk meningkatkan kapasitas runway sebanyak 114 hingga 120 pergerakan per jam. Runway 3 dan runway 2 merupakan parallel runway dimana runway 3 dihubungkan oleh crossing taxiway jika pesawat di runway 3 akan menuju ke terminal. Dalam studi ini dilakukan analisis kesesuaian parallel runway dan pengaruh crossing taxiway terhadap kapasitas runway 2. Analisis panjang runway 3 menggunakan metode FAA, sedangkan untuk kesesuaian jarak pemisah antar runway menggunakan aturan dari ICAO. Perhitungan kapasitas runway menggunakan metode time spcae analysis dan menggunakan permodelan simulasi matematis dengan prinsip Air Traffic Separation. Hasil analisis perhitungan didapatkan evaluasi panjang runway yang dibutuhkan sebesar 3200 m sehingga perlu dilakukan penambahan. Untuk analisis jarak pemisah runway didapatkan jarak 500 meter, untuk pendekatan non-instrument sudah terpenuhi sedangkan untuk pendekatan instrument tidak terpenuhi. Dari analisis simulasi didapatkan kapasitas runway 2 menurun dari 42 pergerakan menjadi 30 pergerakan sedangkan pada runway 3 didapatkan pergerakan sebesar 29 pergerakan. Total pergerakan menjadi 59 pergerakan, sehingga adanya penambahan runway 3 hanya meningkatkan operasi penerbangan sebanyak 17 pergerakan dibandingkan jika operasi hanya menggunakan runway 2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".