PELATIHAN BUDIDAYA TEBU BAGI PETANI TEBU RAKYAT DI JAWA TIMUR
Bibliographic record
Abstract
Tingkat produktivitas tebu di Indonesia, khususnya di Jawa Timur, masih relatif rendah akibat terbatasnya pemahaman teknis petani terkait budidaya yang sesuai dengan prinsip good agricultural practices (GAP). Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi teknologi budidaya tebu bagi petani binaan Dinas Perkebunan Provinsi Jawa Timur melalui pendekatan penyuluhan kelas dan praktik lapangan. Pelatihan dilaksanakan pada tanggal 4-7 Mei 2024 di Pusat Penelitian Perkebunan Gula Indonesia (P3GI) Pasuruan dan melibatkan 26 petani dari Lumajang dan Situbondo. Evaluasi menggunakan kuesioner pra dan pasca-kegiatan menunjukkan peningkatan pemahaman peserta dari 55% menjadi 85%. Respons positif dari peserta juga ditunjukkan melalui minat untuk menerapkan teknik baru, seperti penggunaan varietas unggul dan pola pemupukan efisien. Tantangan utama yang diidentifikasi meliputi keterbatasan modal dan akses terhadap sarana produksi. Hasil kegiatan ini menunjukkan bahwa kombinasi penyampaian teori dan praktik langsung efektif dalam meningkatkan kapasitas teknis petani. Dukungan lanjutan dari pemerintah dan pemangku kepentingan lainnya sangat diperlukan untuk memastikan keberlanjutan adopsi inovasi di tingkat petani
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".