ANALISIS HUBUNGAN BIAYA PRODUKSI KELAPA SAWIT TERHADAP PENDAPATAN PETANI DI DESA PULO BAYU KECAMATAN HUTABAYU RAJA, KABUPATEN SIMALUNGUN ORGANIK
Bibliographic record
Abstract
The aim of study is to 1) determine the productivity of oil palm in the study area, 2) determine the income of oil palm farming in the study area and 3) determine the relationship of the cost of oil palm production with the income of oil palm farmers in the study area. This research was conducted in Pulo Bayu village, Hutabayu Raja district, Simalungun regency. The population in this study were farmers who worked on oil palm plants in Pulo Bayu village with a field area ranging from 0.5-10 Ha and the age of the plants between 3.5-18 years. The number of samples in this study were 30 oil palm farmers. Data analysis was performed descriptively and using simple linear regression analysis. The result showed that the productivity of oil palm plants in the study area was classified as low. The production of oil palm farming in the study area is 82,342.80 kg/year or 1,744.76 kg/ ha (1.75 tons/ha/month). This is lower than the average CPO productivity of smallholder estates 2.5 tons/ ha/month. Net income of oil palm farmer in the study area is Rp 1,308,973.06/ha/month is classified as low because it is lower than the UMR of Simalungun regency (Rp 2,224,036.00/ month). Farming production costs significantly have a positive linear effect on the income of oil palm farmers in the study area.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".