SIFAT FISIKO-KIMIA DAN MUTU ORGANOLEPTIK BAKSO BROILER DENGAN MENGGUNAKAN TEPUNG UBI JALAR (Ipomoea batatas L)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui efek berbagai prosentase tepung ubi jalar terhadap sifat fisiko-kimia dan mutu organoleptik bakso broiler. Variabel yang diamati adalah sifat fisik dan Sifat Organoleptik bakso. Data untuk semua peubah dianalisis menurut prosedur analisis ragam (ANOVA) dari Rancangan Acak Lengkap dan untuk mengetahui perlakuan mana yang berbeda nyata secara statistik dilakukan pengujian dengan Uji Wilayah Berganda Duncan. Untuk uji organoleptik digunakan metode Scoring Deffrent test dengan jumlah panelis 35 orang. Hasil penelitian ini menunjukan bahwa penambahan presentase tepung ubi jalar hingga 20% berpengaruh sangat nyata (P<0.01) terhadap Daya Mengikat Air dan Kadar Air. Tapi tidak berpengaruh nyata (P>0.05) terhadap pH. Hasil Organoleptik menunjukkan bahwa perlakuan memberikan pengaruh yang sangat nyata (P<0.01) terhadap tekstur, kekenyalan, dan citarasa. Tapi tidak berpengaruh nyata (P>0.05) terhadap aroma. Berdasarkan hasil penelitian dan analisa data dapat disimpulkan bahwa penambahan tepung ubi jalar sebagai filler hingga prosentase 20% menghasilkan bakso broiler dengan sifat fisiko-kimia yang baik dan secara organoleptik dapat diterima oleh konsumen.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".