ANALISIS PERUBAHAN PEMANFAATAN LAHAN BERDASARKAN MODEL SPASIAL HARGA LAHAN DI KECAMATAN BANDAR KEDUNG MULYO KABUPATEN JOMBANG
Bibliographic record
Abstract
Pembangunan gerbang TOL Jombang di Kecamatan Bandar Kedung Mulyo menimbulkan harga lahan bertambah serta timbul pergantian pemanfaatan lahan. Penentuan pergantian pemanfaatan lahan menggunakan 3 model spasial harga lahan ialah. (1) Analisis Delphi buat mengenali aspek apa saja yang memastikan harga lahan, (2) Metode analisis regresi spasial buat model spasial harga lahan, serta (3) Metode analisis Query Builder buat hasil peta pergantian pemanfaatan lahan di Kecamatan Bandar kedung mulyo. Ada 12 aspek penentu harga lahan dari hasil analisis Delphi. Aspek yang mempunyai pengaruh positif dalam model tersebut ialah rencana jaringan jalur, sarana perdagangan serta jasa, serta jalan angkutan universal. Aspek yang mempunyai pengaruh negatif ialah sarana peribadatan, sarana pembelajaran, sarana kesehatan, sarana perkantoran, sungai, jalur kolektor, rencana kawasan industri, kawasan permukiman, rencana kawasan permukiman, serta interchange gerbang TOL. Model spasial menampilkan kalau harga lahan besar ada di dekat interchange gerbang TOL serta harga lahan rendah di daerah perbatasan Kecamatan Bandar kedung mulyo. Kata Kunci : Interchange gerbang TOL Jombang, potensi perubahan pemanfaatan lahan, harga lahan
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".