Inovasi Pengolahan Buah Naga Merah Menjadi Keripik Renyah di Desa Ganra Kabupaten Soppeng
Bibliographic record
Abstract
Pelaksanaan pelatihan pengolahan buah naga menjadi keripik renyah di Desa Ganra Kabupaten Soppeng bertujuan untuk menambah pengetahuan dan meningkatkan keterampilan masyarakat setempat. Buah naga hasil budidaya petani hanya di petik dan di jual ke pasar lokal tanpa adanya pengolahan, bahkan harga menjadi lebih sangat murah ketika musim panen. Dalam kegiatan ini, tim pengabdian kepada masyarakat dari Univeristas Puangrimaggalatung melakukan pelatihan kepada masyarakat terkait pengolahan buah naga merah dengan menggunakan teknologi. Adapun pelatihan ini meliputi pengenalan teknologi, praktik penggunaan alat dan strategi pemasaran produk. Pelatihan pengabdian kepada masyarakat ini guna meningkatkan pengetahuan dan keterampilan masyarakat dalam mengolah menjadi produk, membuat kemasan menarik hingga pemasaran produk melalui e-commerce. Dari kegiatan pelatihan ini menghasilkan produk keripik renyah dari buah naga merah, menggunakan kemasan yang berkualitas, dan jangkauan pemasaran yang lebih luas, serta menambah pendapatan bagi masyarakat setempat. Alat teknologi yang digunakan merupakan teknologi tepat guna yang dapat membantu masyarakat Desa Ganra dalam membuat produk keripik buah naga dan kemasan yang berdaya saing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".