Implementasi Penataan Ruang di Kawasan Dataran Tinggi Dieng Kabupaten Banjarnegara
Bibliographic record
Abstract
Dataran Tinggi terletak di 6 wilayah, yaitu Banjarnegara, Wonosobo, Pekalongan, Batang, Temanggung, dan Kendal. Kawasan yang diprioritaskan berada di Kabupaten Banjarnegara. Pemrioritasan ini didasarkan pada potensi kerusakan lingkungan dan pemanfaatan ruang di dalamnya. Penelitian ini bertujuan untuk menganalisis kondisi penggunaan lahan eksisting beserta peruntukan ruang kawasan sesuai RTRW Kabupaten Banjarnegara Tahun 2011-2031. Selain itu, dianalisis juga kesesuaian antara keduanya, berpedoman pada kriteria dalam Permen ATR/BPN Nomor 6 Tahun 2017 beserta faktor-faktor yang menyebabkan. Metode yang digunakan adalah metode kualitatif. Kondisi eksisting diinterpretasi melalui Citra Quickbird dan survey lapangan. Hasil penelitian menunjukan 11 jenis penggunaan lahan eksisting yang teridentifikasi dengan luasan terbesar kebun sayur dan ada 10 jenis peruntukan ruang dengan luasan terbesar lahan pertanian hortikultura. Tingkat kesesuaian keduanya tergolong tinggi. Dari kriteria jenis dan besaran, ketidaksesuaian memiliki persentase sebesar 9,36%. Dari segi dampak, pemanfaatan ruang menimbulkan dampak lokal dan regional. Kondisi ini dipengaruhi oleh faktor alami, faktor sosial, serta faktor lainnya (faktor teknis dan faktor regulasi). Kata-Kata Kunci : penggunaan lahan, peruntukan ruang, implementasi penataan 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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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".