Strategi Manajemen Dan Rekayasa Lalu Lintas Di Ruas Jalan Jenderal Sudirman Kota Kupang Dengan Menggunakan Analisis SWOT
Bibliographic record
Abstract
Permasalahan lalu lintas jalan raya merupakan suatu permasalahan yang kompleks. Pertumbuhan jumlah penduduk menyebabkan kebutuhan akan transportasi lalu lintas semakin meningkat. Kondisi arus lalu lintas di Jl. Jenderal Sudirman terpantau sudah mulai tidak stabil, karena adanya titik-titik rawan macet pada ruas jalan tersebut. Masalah lalu lintas disebabkan karena adanya on street parking ilegal, dan juga parkir ganda dimana kendaraan yang parkir di sebelah kendaraan yang sedang parkir pada ruas jalan. Sehingga perlu adanya manajemen dan rekayasa lalu lintas dengan mengetahui tingkat pelayanan dan kecepatan perjalanan. Semua data yang sudah diperoleh akan di analisis dengan merumuskan kekuatan dan peluang, juga kelemahan dan ancaman dalam analisis SWOT. Dari hasil survei lapangan pada ruas jalan jenderal sudirman dan analisis data menggunakan analisis SWOT, peluang dan ancaman yang dikendalikan oleh kekuatan dan kelemahan dalam matriks SWOT telah merumuskan strategi manajemen dan rekayasa lalu lintas untuk mengatasi permasalahan lalu lintas yaitu; Manajemen kapasitas, manajemen prioritas dan manajemen permintaan.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".