KARAKTERISTIK MI TINGGI ANTIOKSIDAN DARI DAUN KELOR (Moringa oleifera L.) DAN DAUN BELUNTAS (Pluchea indica L.)
Bibliographic record
Abstract
Tanaman kelor (Moringa Oleifera) dan beluntas (Pluchea Indica) mengandung antioksidan yang tinggi sehingga bisa dimanfaatkan sebagai makanan fungsional. Tujuan dari penelitian ini adalah untuk mengetahui karakteristik mi yang dibuat dari eksrak daun kelor dan beluntas. Penelitian ini menggunakan rancangan acak lengkap non faktorial dengan 5 variasi formula pembuatan mi. Paramater yang diamati meliputi analisis warna, antioksidan dan uji sensoris. Data hasil penelitian dianalisis menggunakan uji F pada taraf signifikasi 5% apabila ada beda nyata dilanjutkan dengan uji DMRT?5% dengan bantuan sofware SPSS versi 21. Hasil penelitian menunjukkan bahwa penggunaan daun kelor dalam bentuk bubur dapat meningkatkan kandungan antioksidannya dengan nilai 11,63.%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".