EVALUASI KINERJA JALAN RAYA LEUWILIANG - KABUPATEN BOGOR
Bibliographic record
Abstract
Tujuan penelitian adalah mengevaluasi konerja pengaruh penggunaan lahan terhadap kemacetan, gangguan samping jalan dan titik konflik persimpangan, dan kondisi lalu lintas serta tingkat pelayanan jalan di Jalan Raya Leuwiliang Kabupaten Bogor.Berdasarkan hasil analisis diketahui penggunaan lahan yang paling tinggi menghasilkan bangkitan lalu lintasnya adalah pasar Leuwiliang yang merupakan pusat koleksi dan distribusi hasil pertanian maupun peternakan dalam kota maupun dari luar Kota Kecamatan Leuwiliang, khususnya bagian Wilayah Pengembangan (WP) Barat. Gangguan samping yang terjadi di Jalan Raya Leuwiliang diakibatkan oleh PKL (pedagang kaki lima) yang berada di kiri kanan jalan yang menempati trotoar dan bahu jalan untuk tempat berjualan. Disamping itu adanya parkir on street di kiri kanan jalan Raya Leuwiliang dengan sudut parkir 0 yang dapat mengurangi kapasitas jalan sebesar 32 % atau 1,44 meter. Terjadinya titik konflik pada ketiga persimpangan yaitu persimpangan Puraseda, persimpangan Karehkel, dan persimpangan Setu yang ada pada jalan Raya Leuwiliang akibat dari tingginya pergerakan kendaraan dan geometri persimpangan yang tidak sesuai dengan standar.Rekomomendasi studi, meningkatkan kapasitasjalan Leuwiliang dan perbaikan geometri dari persimpangan yang ada, serta perlu jalan lingkar pada jangka panjang.Kata kunci : Transport, lalu lintas, kemacetan
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".