Strategi Pengembangan Agribisnis Hortikultura di Wilayah Pedesaan
Bibliographic record
Abstract
Status : PostprintProgram-program pengembangan agribisnis di wilayah pedesaan masih menyisakan permasalahan mendasar yaitu harga sarana produksi pertanian terus meningkat, sementara harga produk pertanian primer sangat fluktuatif. Kondisi ini terjadi karena posisi tawar petani yang masih lemah di antara pelaku agribisnis lainnya. Penelitian ini bertujuan untuk mengukur kelayakan usahatani dan menemukan strategi pengembangan agribisnis hortikultura di Kabupaten Simalungun, Sumatera Utara. Dengan mengambil 40 rumah tangga sampel, kelayakan usahatani diukur dengan Revenue Cost Ratio (RCR) dan strategi pengembangan ditentukan melalui Analisis SWOT. Urutan kelayakan komoditas adalah kentang, cabai merah, kubis, tomat, dan jeruk manis. Hasil analisis SWOT untuk pengembangan agribisnis hortikultura mengutamakan strategi W-O yaitu mengubah strategi melalui: kemitraan pemasaran, pengembangan sumber air di usahatani, peningkatan kualitas jalan desa dan jalan usahatani, pengembangan kios sarana produksi di perdesaan, peningkatan penyuluhan pertanian, penataan zonasi dan pola tanam komoditas unggulan, pengembangan agroindustri skala rumah tangga dan skala kecil di perdesaan, serta pengembangan fasilitas kebun bibit dan lahan demplot.Artikel ini diterbitkan pada Prosiding Seminar Ilmiah Nasional Dies Natalis ke-64 Universitas Sumatera Utara, Medan, 18-19 Agustus 2016, hal. 63-70, ISBN 979 458 916 0
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.182 | 0.041 |
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".