ANALISIS DAMPAK LALU LINTAS PEMBANGUNAN TRANSIT ORIENTED DEVELOPMENT (TOD) GREEN WALK STATION BEKASI TIMUR
Bibliographic record
Abstract
Pembangunan yang dilakukan oleh PT. Adhi Karya berupa Kawasan Transit Oriented Development (TOD) Green Walk Station seluas 19,6 Ha (BIA, 2018), pada salah satu Kecamatan Bekasi yang luas wilayahnya sekitar 13,49 km 2 (BPS, 2017) yaitu Bekasi Timur. Pembangunan kawasan ini akan membangkitkan pergerakan yang baru pada jaringan jalan di sekitarnya tentu saja akan berdampak negatif pada lalu lintas disekitarnya. Dampak negatif ini meliputi penambahan volume arus lalu lintas yang menyebabkan kemacetan di ruas jalan dan persimpangan sekitar kawasan, apabila tidak dilakukan penataan manajemen lalu lintas di sekitar kawasan tersebut. Tujuan dari penelitian ini akan melakukan Analisis Dampak Lalu Lintas (Andall) untuk meminimalisir kemacetan akibat hadirnya kawasan tersebut. Jenis penelitian yang digunakan dalam penelitian ini yaitu penelitian kuantitatif. Dari analisis yang didapatkan, tingkat pelayanan ruas jalan dan simpang Apill eksisting atau Skenario Do Nothing menunjukkan rata-rata tingkat D. Setelah dilakukan Rekomendasi atau Skenario Do Something berupa pengaturan waktu siklus, perpindahan moda angkutan dan pelebaran jalan, maka tingkat pelayanannya rata-rata menjadi tingkat C. disimpulkan bahwa Rekomendasi atau Do Something dapat dapat membantu kinerja ruas jalan dan simpang Apill.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".