Formulasi dan Evaluasi Permen Jelly dari Ekstrak Daun Pegagan dan Rimpang Kunyit untuk Kesehatan kardiovaskular
Bibliographic record
Abstract
Hipertensi merupakan penyakit tidak menular yang menjadi salah satu penyebab kematian di dunia. Hipertensi diawali dengan meningkatnya tekanan darah secara terus menerus. Kombinasi dari tanaman pegagan dan kunyit mampu menekan tekanan darah sistolik dan diastolik serta mampu memperbaiki kekakuan arteri. Tujuan penelitian ini mengetahui stabilitas formulasi sediaan sediaan permen jelly dari ekstrak daun pegagan dan rimpang kunyit. Formula permen jelly dibuat menjadi 3 formula dengan konsentrasi penambahan ekstrak daun pegagan dan rimpang kunyit sebesar 3,5 g dengan variasi penggunaan karagenan. Penelitian dianalisis statistik Kruskal wallis. Pengujian permen jelly meliputi uji organoleptis, uji kadar air (metode oven), uji keseragaman bobot, dan uji kesukaan (metode hedonik). Uji organoleptik ketiga formula memiliki warna coklat dengan aroma khas tutty frutty, formula I memiliki rasa manis sedikit pahit dan formula II dan III memiliki rasa manis, formula I dan III memiliki tekstur kenyal sedangkan formula II memiliki tekstur agak kenyal, uji kadar air formula I 3,5%, formula II 4,6% dan formula III 5%, uji keseragaman bobot ketiga formula memenuhi persyaratan uji keseragaman bobot menurut FI III. Dari ketiga formula, formula III memiliki tekstur dan rasa yang disukai responden dengan persentase kadar air sebesar 5% dan memenuhi persyaratan keseragaman bobot.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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 | 0.002 |
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".