Identifikasi Potensi Pengembangan Kegiatan Pertanian dalam Mendukung Perwujudan Food Estate
Bibliographic record
Abstract
Abstract. Food estate development is one way to maintain food security in Indonesia. The current phenomenon is the need for food as a basic human need which will continue to increase over time, besides that it is also necessary to limit food imports and continue to prioritize food with regional independence. Therefore, the Government through the Ministry of Defense is currently planning The 5 locations to be developed as food estates include Kertajati Village, Kertajati District, Majalengka Regency. This village is considered potential because Mekarjaya Village itself has an area of 25% of the total plantation area in Kertajati District. The area for plantations in Mekarjaya Village itself is recorded at 2237.502 Ha. This area is the largest compared to 13 other villages in Kertajati District, Majalengka Regency. The purpose of this research is to identify the development of agricultural activities in supporting the realization of food esate activities. The stages of analysis carried out in this study include Geospatial analysis, On farm Analysis and Off Farm Analysis which have the aim of assessing from these three aspects whether Mekarjaya Village is suitable to be used as an Agricultural Area in supporting food estate activities and it was found that Mekarjaya Village is suitable for used as a food estate area but there are still many things that need to be repaired or developed. Abstrak. Pengembangan food estate merupakan salah satu cara dalam upaya dalam menjaga ketahanan pangan yang ada di Indonesia. Fenomena yang terjadi saat ini adalah kebutuhan akan pangan sebagai basic human needs yang seiring dengan berjalanya waktu akan terus meningkat, selain itu juga perlu adanya pembatasan mengenai impor pangan dan tetap mengedepankan pangan dengan kemandirian daerah, Maka dari itu Pemerintah melalui Kementerian Pertahanan saat ini sedang merencanakan 5 lokasi untuk dikembangkan sebagai food estate diantaranya adalah Desa Kertajati Kecamatan Kertajati Kabupaten Majalengka. Desa ini dianggap potensial karena Desa Mekarjaya sendiri memliki luasan 25% dari total luas perkebunan di Kecamatan Kertajati. Tercatat luasan untuk perkebunan di Desa Mekarjaya sendiri yaitu sebesar 2237,502 Ha, Luasan Tersebut merupakan yang terbesar bila dibandingkan dengan 13 Desa lainnya yang berada di Kecamatan Kertajati Kabupaten Majalengka. Tujuan untuk peneleitian ini adalah mengidentifikasi pengembangan kegiatan pertanian dalam mendukung perwujudan kegiatan food esate. Tahapan Analisis yang dilakukan pada kajian ini diantaranya adalah analisis Geospasial, Analsisis On farm dan Analisis Off Farm yang memiliki tujuan untuk menilai dari ketiga aspek tersebut Desa Mekarjaya apakah cocok untuk dijadikan Kawasan Pertanian dalam mendukung kegiatan food estate dan di dapati hasil bahwa desa mekarjaya cocok untuk dijadikan Kawasan food estate tetapi masih banyak hal hal yang harus diperbaiki atau dikembangkan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".