Dampak Urbanisasi pada Lahan Pertanian: Analisis Spasial di Kecamatan Godean dan Mlati Kabupaten Sleman
Bibliographic record
Abstract
The area of agricultural land in Godean District and Mlati District experienced a significant decline from 2015 to 2022 due to urbanization, amounting to 122 ha and 175.01 ha, respectively. Apart from that, in these two sub-districts, part of the area is part of the Yogyakarta Urban Area (KPY), and the other part is a strategic area with a food security function. This research aims to identify the spatial correlation of agricultural to non-agricultural land conversion in Godean District and Mlati District. Identification of spatial correlations of land conversion was carried out using Average Nearest Neighbor, Spatial Autocorrelations (Morans I), and Cluster and Outlier (Anselin Local Morans I) analysis. The results of the research show that agricultural land that has experienced land conversion in both locations has a tendency to be clustered with a longitudinal spreading pattern (ribbon development). Tlogoadi Village in Mlati District shows a high-sspatial relationship, and Sidoluhur and Sidoagung Villages show a low-sspatial relationship. It was concluded that the land undergoing conversion at the research location has a fairly strong spatial correlation and characteristics that tend to be similar. Luas lahan pertanian di Kecamatan Godean dan Kecamatan Mlati mengalami laju penurunan yang cukup signifikan dari tahun 2015-2022 akibat arus urbanisasi, masing-masing sebesar 122 Ha dan 175,01 Ha. Selain itu, pada kedua kecamatan ini sebagian wilayahnya menjadi bagian dari Kawasan Perkotaan Yogyakarta (KPY) dan sebagian lainnya menjadi kawasan strategis dengan fungsi ketahanan pangan. Penelitian ini bertujuan untuk mengidentifikasi korelasi spasial alih fungsi lahan pertanian ke non pertanian di Kecamatan Godean dan Kecamatan Mlati. Identifikasi korelasi spasial alih fungsi lahan dilakukan dengan analisis Average Nearest Neighbor, Spatial Autocorrelations (Morans I) dan Cluster and Outlier (Anselin Local Morans I). Hasil penelitian menunjukkan bahwa lahan pertanian yang mengalami alih fungsi lahan di kedua lokasi mempunyai kecenderungan mengelompok (clustered) dengan pola perembetan memanjang (ribbon development). Desa Tlogoadi di Kecamatan Mlati menunjukkan hubungan spasial High-High serta Desa Sidoluhur dan Sidoagung menunjukkan hubungan spasial Low-Low. Disimpulkan bahwa lahan yang mengalami alih fungsi pada lokasi penelitian memiliki korelasi spasial yang cukup kuat dan karakteristik yang cenderung memiliki kesamaan.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".