Analisis Faktor-Faktor Yang Berpengaruh Dalam Pemilihan Lokasi Relokasi Pedagang Kaki Lima di Kawasan Pasar Pagi, Kota Samarinda
Bibliographic record
Abstract
Sesuai dengan program prioritas RPJMD Kota Samarinda tahun 2016-2021 dalam penataan Pedagang Kaki Lima di kawasan perdagangan dan jasa, bentuk usaha yang sudah diterpakan dalam upaya penataan Pedagang Kaki Lima di kawasan Pasar Pagi dengan melakukan penertiban dan penggusuran. Namun yang menjadi permasalahan adalah karena Pedagang Kaki Lima yang telah digusur terus kembali pada lokasi yang sama untuk berdagang. Sehingga sebagai salah satu langkah awal dalam penataan maka perlu adanya identifikasi karakteristik PKL serta analisis faktor-faktor yang berpengaruh dalam pemilihan lokasi untuk relokasi bagi Pedagang Kaki Lima di kawasan Pasar Pagi. Metode yang digunakan adalah statistik deskrptif dan analisis Delphi. Hasil menunjukkan bahwa karakteristik PKL di Pasar Pagi teridentifikasi dalam tiga kelompok yang dibagi berdasarkan jenis dagangan yaitu Kelompok I, Makanan/Minuman Siap Saji; Kelompok II : Non-Makanan, Kelompok III : Jasa dan faktor-faktor yang berpengaruh dalam penataan pedagang kaki lima berdasarkan perspektif stakeholders yaitu (1) Sirkulas; (2) Dekat Permukiman; (3) Aksesibilitas; (4) Ekspansi; (5) Lahan Parkir; (6) Jaringan Listrik; (7) Jaringan Air Bersih; (8) Jaringan Limbah; (9) Lingkungan; (10) Visibilitas; (11) Fungsi Jalan; (12) Akses Pejalan Kaki dan (13) Kebijakan Tata Ruang.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".