KAJIAN KEMAMPUAN LAHAN DALAM MENDUKUNG IMPLEMENTASI RTRW DI KECAMATAN AIRMADIDI
Bibliographic record
Abstract
This research is motivated by the strategic position of Airmadidi District as the capital of North Minahasa Regency and a National Activity Center, which faces significant development pressures. The potential for conflict between development needs and environmental conservation in spatial use necessitates land capability analysis to support the sustainable implementation of the Regional Spatial Plan (RTRW). Therefore, this study focuses on analyzing and mapping land capability classes in Airmadidi District based on their biophysical characteristics. This study uses a quantitative approach with overlay and scoring methods based on Geographic Information Systems (GIS) on secondary data, including rainfall, topography, geology, slope gradient, and disaster vulnerability. The results indicate variations in land capability classes. Most areas fall into the moderately high category (class d), suitable for development with attention to mitigation. Areas with very high capability (class e) were identified in Upper Airmadidi, ideal for activity centers. Meanwhile, areas with moderate capability (class c) in Sawangan and Tanggari are more suitable for conservation. It is concluded that spatial planning in Airmadidi District must refer to this land capability map to rationally guide development and prevent environmental degradation. ABSTRAKPenelitian ini dilatarbelakangi oleh posisi strategis Kecamatan Airmadidi sebagai ibu kota Kabupaten Minahasa Utara dan Pusat Kegiatan Nasional, yang menghadapi tekanan pembangunan signifikan. Adanya potensi konflik pemanfaatan ruang antara kebutuhan pembangunan dengan konservasi lingkungan mendorong perlunya analisis kemampuan lahan untuk mendukung implementasi Rencana Tata Ruang Wilayah (RTRW) secara berkelanjutan. Oleh karena itu, penelitian ini berfokus untuk menganalisis dan memetakan kelas kemampuan lahan di Kecamatan Airmadidi berdasarkan karakteristik biofisiknya. Penelitian ini menggunakan pendekatan kuantitatif dengan metode overlay dan skoring berbasis Sistem Informasi Geografis (SIG) terhadap data sekunder, meliputi curah hujan, topografi, geologi, kemiringan lereng, dan kerawanan bencana. Hasil penelitian menunjukkan adanya variasi kelas kemampuan lahan. Sebagian besar wilayah masuk dalam kategori agak tinggi (kelas d), yang sesuai untuk pengembangan dengan memperhatikan mitigasi. Wilayah dengan kemampuan sangat tinggi (kelas e) teridentifikasi di Airmadidi Atas, ideal untuk pusat kegiatan. Sementara itu, wilayah berkemampuan sedang (kelas c) di Sawangan dan Tanggari lebih cocok untuk konservasi. Disimpulkan bahwa perencanaan ruang di Kecamatan Airmadidi harus mengacu pada peta kemampuan lahan ini untuk mengarahkan pembangunan secara rasional dan mencegah degradasi lingkungan.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".