Penilaian Kesesuaian Penggunaan Tanah dengan Rencana Detail Tata Ruang Kawasan Peruntukan Industri Bagendang
Bibliographic record
Abstract
Salah satu strategi utama untuk mendorong pengembangan industri di Kabupaten Kotawaringin Timur melibatkan implementasi kebijakan tata ruang melalui Rencana Detail Tata Ruang (RDTR) di Kawasan Alih Fungsi Industri Bagendang (KPI Bagendang). Meskipun RDTR KPI Bagendang telah diterbitkan pada tahun 2021, belum ada penelitian lanjutan yang dilakukan terkait dengan kesesuaian penggunaan lahan sebagaimana diatur dalam RDTR. Studi ini bertujuan menilai status penggunaan lahan di KPI Bagendang pada 2022, mengevaluasi kesesuaian dengan ketentuan RDTR KPI Bagendang, dan mengidentifikasi ketidaksesuaian di area tersebut. Metode penelitian deskriptif kualitatif digunakan dalam kerangka keruangan. RDTR KPI Bagendang mengalokasikan 1.358,32 hektar (35,89%) sebagai Kawasan Industri, tetapi yang dominan adalah perkebunan dengan 2.937,39 hektar (77,61%). Evaluasi menunjukkan 3.719,34 hektar (98,26%) sesuai pedoman RDTR, dan 65,68 hektar (1,74%) tidak sesuai. Temuan ini menegaskan bahwa sebagian besar pemanfaatan ruang di KPI Bagendang sesuai rencana, namun ketidaksesuaian terutama dari struktur bangunan di tepi sungai, jalan, dan area yang semula untuk perkebunan. Kata Kunci: Penataan Ruang; Pengendalian Ruang; Pemanfaatan Ruang; Kotawaringin Timur Industrial Development; KPI Bagendang.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".