PRODUKSI & ANALISIS ZAT GIZI MAKRO, MIKRO DAN ASAM LEMAK OMEGA 3 ABON IKAN LAYANG SEBAGAI PANGAN FUNGSIONAL
Bibliographic record
Abstract
Pendahuluan: dari Riset Kesehatan Dasar tahun 2018 menunjukkan di Sulawesi Selatanmengalami peningkatan penyakit tidak menular terutama penyakit diabetes, penyakit jantungkoroner, hipertensi, obesitas usia 18 keatas. World Health Organization (WHO)memperkirakan, pada tahun 2020, penyakit tidak menular menjadi penyebab kematian dankesakitan di dunia. Pangan fungsional yang bermanfaat bagi tubuh dan mengurangi resikoterkena penyakit tidak menular. Salah satu pangan yang memiliki khasiat bagi kesehatanadalah ikan layang, implementasi pangan kedalam bentuk produk abon. Tujuan daripenelitian untuk mengetahui gambaran formula terpilih abon, kandungan energi, zat gizimakro, mikro dan asam lemak omega-3 abon ikan layang. Jenis penelitian ini adalahdeskriptif berbasis laboratorium. Dibuat 3 formula yang masing-masing terdri dari ikanlayang, santan, gula merah dan bumbu penyedap lainnya. Hasil penelitian menunjukkanbahwa dari ketiga formula, formula 3 terpilih sebagai formula terbaik. Adapun kandunganenergi, makro, mikro dan asam lemak omega-3 pada formula terpilih dalam 100 g, untukkandungan energi 60,21 kkal, kandungan karbohidrat 12,83 g, kandungan protein 48,47 g,kandungan lemak 25,26 g, kandungan natrium 0,61mg, kandungan magnesium 0,80 mg, dankandungan asam lemak omega 3 1,1187 mg. Kesimpulan bahwa kandungan zat gizi abonikan layang memiliki peningkatan kandungan setelah diolah menjadi produk, terutama asamlemak omega 3. Disarankan untuk penelitian selanjutnya dapat menguji kembali dengananalisis beberapa kandungan zat gizi lainnya.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".