Arahan Pengendalian Pemanfaatan Ruang Berbasis Mitigasi Bencana di Wilayah Sekitar Sesar Lembang
Bibliographic record
Abstract
Abstract. The area around the Lembang Fault has a high potential for disasters, especially earthquakes and landslides. However, spatial utilization in this area often ignores spatial suitability and disaster mitigation aspects, characterized by the rapid growth of the economic, tourism, and residential sectors in high-risk zones. This study aims to: (1) identify the suitability of spatial utilization to the spatial pattern plan, (2) identify suitability to the risk of earthquakes and landslides, and (3) formulate directions for controlling spatial utilization based on disaster mitigation. The method used is a quantitative approach through Geographic Information System (GIS)-based spatial analysis, supplemented by field interviews as supporting data. The results show that 28.73% of spatial utilization is not according to plan, with the conversion of agricultural land to settlements as the dominant change. Most of the cultivated areas (69.35%) and protected areas (28.01%) are also in the medium to high risk category. Based on these findings, directions for controlling spatial utilization are formulated for 12 combination zones (L1–L6 and B1–B6) with a mitigative approach through incentives, disincentives, and sanctions. These findings are expected to strengthen spatial control policies in disaster-prone areas. Abstrak. Wilayah sekitar Sesar Lembang memiliki potensi tinggi terhadap bencana, khususnya gempa bumi dan gerakan tanah. Namun, pemanfaatan ruang di wilayah ini terdapat yang mengabaikan kesesuaian ruang dan aspek mitigasi bencana, ditandai dengan pertumbuhan pesat sektor ekonomi, wisata, dan permukiman di zona risiko tinggi. Penelitian ini bertujuan untuk: (1) mengidentifikasi kesesuaian pemanfaatan ruang terhadap rencana pola ruang, (2) mengidentifikasi kesesuaian terhadap risiko bencana gempa bumi dan gerakan tanah, serta (3) merumuskan arahan pengendalian pemanfaatan ruang berbasis mitigasi bencana. Metode yang digunakan adalah pendekatan kuantitatif melalui analisis spasial berbasis Geographic Information System (GIS), dilengkapi wawancara lapangan sebagai data pendukung. Hasil menunjukkan bahwa 28,73% pemanfaatan ruang tidak sesuai rencana, dengan konversi lahan pertanian menjadi permukiman sebagai perubahan dominan. Sebagian besar kawasan budidaya (69,35%) dan kawasan lindung (28,01%) juga berada dalam kategori risiko sedang hingga tinggi. Berdasarkan temuan tersebut, dirumuskan arahan pengendalian pemanfaatan ruang untuk 12 zona kombinasi (L1–L6 dan B1–B6) dengan pendekatan mitigasi melalui insentif, disinsentif, dan sanksi. Temuan ini diharapkan dapat memperkuat kebijakan pengendalian ruang di wilayah rawan bencana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".