Penentuan Rute Angkutan Umum berdasarkan Kebutuhan Perjalanan Penduduk di Kawasan Perkotaan Gresik
Bibliographic record
Abstract
Ketimpangan tingkat aksesibilitas angkutan umum di kawasan perkotaan Gresik, rute angkutan umum yang kurang akomodatif terhadap tujuan pergerakan, dan jauhnya jangkauan ke pelayanan angkutan umum, mengindikasikan bahwa rute angkutan umum belum selaras dengan kebutuhan pergerakan penduduk kawasan perkotaan Gresik. Penelitian ini ditujukan untuk menentukan alternatif rute untuk angkutan umum di wilayah kawasan perkotaan Gresik berdasarkan kebutuhan pergerakan penduduknya. Penentuan rute angkutan umum dilakukan dengan mengidentifikasi pola pergerakan penduduk yang diolah menjadi Matriks Asal Tujuan Perjalanan, menentukan prioritas kriteria rute pelayanan angkutan umum melalui Analysis Hierarchy Process (AHP), dan perumusan rute angkutan umum melalui aplikasi TRANETSIM. Hasil penelitian menunjukkan bahwa dalam penentuan rute angkutan umum, pengguna angkutan umum lebih memprioritaskan kemampuan coverage rute dibandingkan dengan total jarak perjalanan. proses penelitian penentuan rute angkutan umum didapatkan 3 rute angkutan umum. Koridor pelayanan rute pertama adalah Terminal Bunder-Suci-Sidokumpul- Randuagung-Terminal Bunder (PP). Koridor pelayanan rute angkutan umum kedua adalah Terminal Bunder-Randuangung (GKB)-Karangpoh-Indro-Terminal Segoromadu (PP). Koridor pelayanan rute angkutan umum ketiga adalah Sub Terminal Segoromadu-Kedanyang-Karangpoh-Sub Terminal Segoromadu (PP)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".