Dampak Ekonomi Alih Fungsi Lahan Pertanian Di Kecamatan Lalabata Kabupaten Soppeng
Bibliographic record
Abstract
ABSTRAKKepadatan penduduk di suatu daerah seringkali memicu terjadinya alih fungsi lahan. Kondisi ini kemudian berdampak pada aspek ekonomi. Penelitian ini bertujuan untuk menganalisis perubahan penggunaan lahan pertanian dan mengidentifikasi faktor serta dampak ekonominya. Studi ini penting untuk memahami efek jangka panjang alih fungsi lahan terhadap keberlanjutan lingkungan dan kesejahteraan masyarakat. Metode yang digunakan meliputi analisis data deskriptif kuantitatif, teknik overlay untuk melihat perubahan penggunaan lahan, serta uji Chi-Square guna menentukan faktor dan dampak ekonomi dari alih fungsi lahan pertanian. Hasil menunjukkan lahan pertanian menurun dari 7625,70 Ha (83,01%) pada 2014 menjadi 7164,44 Ha (77,99%) pada 2024 sementara lahan permukiman meningkat dari 562,86 Ha (6,13%) menjadi 860,38 Ha (9,37%). Faktor utama yang memengaruhi alih fungsi lahan meliputi jumlah dan kepadatan penduduk, pendidikan petani, infrastruktur, harga lahan, pendapatan, kebijakan, dan penyerapan tenaga kerja. Dampak positif dari segi ekonomi adalah terbukanya lapangan pekerjaan baru dan pergeseran struktur ekonomi masyarakat dari sektor pertanian menjadi sektor industri dan jasa. Dampak negatifnya berupa penyusutan lahan pertanian yang mengancam produksi pangan, memicu kenaikan harga bahan pokok, dan mengganggu kestabilan ekonomi petani. Penelitian ini penting bagi negara berkembang untuk memahami dampak jangka panjang alih fungsi lahan terhadap keberlanjutan lingkungan dan kesejahteraan masyarakat, serta sebagai pertimbangan dalam pengambilan keputusan lahan.Kata Kunci: Perubahan Penggunaan lahan, Analisis Spasial, Alih Fungsi Lahan Pertanian, Dampak Ekonomi ABSTRACTPopulation density in an area often triggers land use change. This condition then has an impact on economic aspects. This study aims to analyze changes in agricultural land use and identify the factors and economic impacts. This study is important for understanding the long-term effects of land use change on environmental sustainability and community welfare. The methods used include quantitative descriptive data analysis, overlay techniques to observe changes in land use, and Chi-Square tests to determine the factors and economic impacts of agricultural land use change. The results show that agricultural land decreased from 7625.70 Ha (83.01%) in 2014 to 7164.44 Ha (77.99%) in 2024, while residential land increased from 562.86 Ha (6.13%) to 860.38 Ha (9.37%). The main factors influencing land conversion include population size and density, farmer education, infrastructure, land prices, income, policy, and labor absorption. The positive economic impact is the creation of new jobs and a shift in the community's economic structure from the agricultural sector to the industrial and service sectors. The negative impact is the reduction of agricultural land, which threatens food production, triggers an increase in the price of basic commodities, and disrupts the economic stability of farmers. This research is important for developing countries to understand the long-term impact of land conversion on environmental sustainability and welfare, as well as for consideration in land use decision-making.Keywords: Land Use Change, Agricultural Land Conversion, Spatial Analysis, Economic Impact
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".