Analisis Usahatani Ubi Kayu “Varietas Daplang” (Manihot utilissima Pohl) Di Petak 15 Wilayah Sekuro Kecamatan Mlonggo Kabupaten Jepara
Bibliographic record
Abstract
ABSTRAK Penelitian ini betujuan untuk mengetahui pendapatan, kelayakan, dan pengaruh faktor produksi Usahatani Ubi kayu “varietas daplang” di petak 15 wilayah Sekuro kecamatan Mlonggo kabupaten Jepara pada bulan Januari-Februari 2020. Metode yang digunakan dalam penelitian ini adalah metode diskriptif analisis,. Metode pengambilan sampel dengan metode sampling sistematis. Metode penghitungan data menggunakan: biaya produksi, penerimaan, dan pendapatan. Kelayakan usahatani dapat dihitung menggunakan: RCR, BEP, dan ROI. Cara untuk mengetahui pengaruh sarana produksi dan tenaga kerja terhadap pendapatan digunakan analisis regresi linier berganda. Dari hasil penelitian menunjukkan: pendapatan usahatani ubikayu “varietas daplang” sebesar Rp. 13.024.066,67 per hektar. Kelayakan RCR 1,55, BEP(Rp)= 904,9 (real Rp. 1400,00/Kg), BEP(Q)= 15.815,21 Kg (real 26.247 Kg), ROI= 155%. Analisis pengaruh sarana produksi dan tenaga kerja terhadap pendapatan usahatani ubikayu diperoleh persamaan Y= + 13603667,16 +7.687 X1 -379 X2 -1.012 X3 -849ns X4. Kesimpulan usahatani ubikayu varieras daplang di petak 15 wilayah Sekuro kecamatan Mlonggo kabupaten Jepara menguntungkan dan layak untuk diusahakan, secara simultan sarana produksi dan tenaga kerja berpengaruh sangat nyata terhadap pendapatan usahatani ubikayu “varietas daplang”, namun secara parsial hanya bibit, pupuk, dan transportasi yang berpengaruh nyata terhadap pendapatan usahatani ubikayu “varietas daplang”. Kata kunci: analisis, usahatani, ubikayu “varietas daplang”.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".