Analisis Peningkatan Kinerja Gerbang Tol Cempaka Putih
Bibliographic record
Abstract
Penelitian kali ini dilakukan untuk mengetahui kinerja Gerbang Tol Cempaka Putih, yang dimana pada saat ini penyelesaian dilakukan untuk meningkatkan kinerja Gerbang Tol Cempaka Putih demi memperpendek antrean kendaraan pada Gerbang Tol Cempaka Putih. Penelitian ini menggunakan metode kuantitatif dengan melakukan observasi dan pengumpulan data primer dan sekunder. Maka didapatkan hasil penelitian ini dengan 4 jenis analisa yaitu banyak 1434 kendaraan/jam dengan kapasitas gardu tol maksimal sebesar 423 kendaraan/jam, waktu tundaan rata-rata kendaraan sebesar 112,31 detik, panjang antrean kendaraan yang terjadi yaitu sebesar 178,61 meter. Berdasarkan hasil yang diperoleh tersebut, panjang antrean kendaraan pada Gerbang Tol Cempaka Putih kondisi eksisting belum memenuhi standar pelayanan minimum jalan tol dengan intensitas lalu lintas berdasarkan perhitungan manual memiliki nilai lebih besar dari 1, dan kondisi panjang antrean rata-rata pada satu tahun yang akan datang berdasarkan hasil pendugaan lalu lintas dan analisis aplikasi perangkat lunak PTV VISSIM yaitu mengalami penurunan signifikan dengan kondisi eksisting, sehingga dapat diketahui bahwa dibutuhkan solusi penerapan sistem transaksi SLFF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".