Dampak Program Terhadap Peningkatan Produksi Kedelai di Jawa Tengah
Bibliographic record
Abstract
Abstract Soybean is one of the main staple food in Indonesia, but domestic soybean production has decreased every year, causing the high dependence on imported soybeans. The government has carried out several programs to increase soybean production, though it been far from expectations. Therefore, this study aims to identify the impact of programs and activities to increase soybean production on meeting soybean needs and farmer participation. The research uses qualitative and evaluative methods by taking locations in Central Java. Evaluation of programs aimed at increasing soybean production. The data used are secondary data and primary data obtained from interviews, then the data were analyzed using an interactive model, namely: data collection, data reduction, data presentation, and interrelated conclusions. Results show that the soybean planting area fluctuate that a decrease occurred in 2019, accounting for 158%. While the achievement of the harvested area was not in line with the planted area because there was a crop failure, and the harvest time shifted to the following year. Soybean availability has decreased, otherwise, demand has continued to increase throughout the year despite a decline in soybean consumption in 2020 and 2021. The highest soybean planting area was obtained from government programs, with the largest participation occurred in 2020 at 27%. Finally, farmers' participation in fulfilling new soybean needs is 4.21%. Conclusion: The dependence of production achievement on government programs reaches 87.48% per year by meeting the needs of 26.32% per year. The participation of farmers independently contributed 4.21% to fulfill the needs of soybeans.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".