Analisis Transportasi untuk Lokasi Pengisian Bahan Bakar Moda Kendaraan Listrik
Bibliographic record
Abstract
Dengan teknologi yang diciptakan oleh kendaraan kendaraan listrik berbasis baterai menjadi salah satu kendaraan yang banyak diminati oleh masyarakat kelas atas atau ekonomi diatas rata rata. Tujuan penelitian adalah untuk mengetahui penentuan titik lokasi sarana pengisian kendaraan listrik dengan menggunakan analisis GIS (overlay) dan untuk mengidentifikasi penentuan titik prioritas lokasi sarana pengisian kendaraan listrik di Segitiga Emas Setiabudi menggunakan analisis GIS (Overlay dan Kernel Density). Berdasarkan hasil penelitian dapat diketahui bahwa terdapat 4 (empat) titik lokasi yang dapat dgunakan seagai stasiun pengisian kendaraan listrik yang meliputi Ciputra World 1 di Jalan Dogol, Parkir Komersil di Jalan Karet Pedurenan, Parkir Komersil Wisma Bakrie di Jalan H.R Rasuna Said dan Parkir Komersil di World Trade Centre di Jalan Jenderal Sudirman, dengan penentuan berdasarkan tempat parkir, partisipasi kebutuhan daya dan penggunaan lahan. Skala prioritas untuk lokasi stasiun pengisian kendaraan listrik di Kawasan Segitiga Emas yaitu Ciputra World 1 di Jalan Dogol dengan prioritas rendah, Parkir Komersil di Jalan Karet Pedurenan dengan prioritas rendah, Parkir Komersil Wisma Bakrie di Jalan H.R Rasuna Said dengan prioritas rendah dan Parkir Komersil di World Trade Centre di Jalan Jenderal Sudirman dengan prioritas Tinggi.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".