ANALISIS TINGKAT PELAYANAN DAN KENYAMANAN JALUR PEDESTRIAN DI JALAN GAJAH MADA, DIPONEGORO DAN PAHLAWAN BERDASARKAN PERSEPSI PENGGUNA
Bibliographic record
Abstract
Jalan Gajah Mada, Diponegoro dan Pahlawan merupakan kawasan yang memiliki tingkat keramaian yang tinggi karena adanya pusat kegiatan perdagangan, jasa dan perkantoran di Sidoarjo. Permasalah yang terjadi di jalan tersebut adalah banyaknya parkir liar dan rusaknya jalur pedestrian. Penelitian ini bertujuan untuk mengetahui kondisi fisik jalur pedestrian, mengetahui tingkat pelayanan serta tingkat kenyamanan berdasarkan persepsi pengguna. Metode penelitian menggunakan deskriptif kualitatif dan kuantitatif dengan teknik analisis LOS (Level Of Service) dan AHP (Analysis Hierarcy Process). Metode pengumpulan data dilakukan dengan cara survey lapangan, sebar kuisioner dan dokumentasi. Hasil dari penelitian ini ialah kondisi fisik jalur pedestrian di Jalan Gajah Mada, Diponegoro dan Pahlawan telah sesuai dengan pedoman. Jika ditinjau dari tingkat pelayanan Jalan Gajah Mada tergolong tingkat B, sedangkan Jalan Diponegoro dan Pahlawan tergolong tingkat pelayanan A. Para pengguna merasa aman dan nyaman saat berjalan di jalur pedestrian Jalan Gajah Mada, Diponegoro dan Pahlawan. Arahan pengembangan untuk jalur pedestrian berdasarkan hasil AHP yaitu alternatif 1 (satu) relokasi area parkir agar tidak menggunakan jalur pedestrian 40,8%, alternatif 2 (dua) Perbaikan kondisi fisik jalur pedestrian 42% dan alternatif 3 (tiga) Peningkatan keterseediaan elemen pendukung jalur pedestrian 17,2%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".