Identifikasi Formaldehida Dalam Tahu Dan Mie Basah Pada Produk Pedagang Jajanan Di Sekitar Kampus Universitas YARSI Jakarta
Bibliographic record
Abstract
Formalin (larutan Formaldehida 37% dalam air) sering disalahgunakan fungsinya untuk mengawetkan makanan / bahan makanan seperti tahu dan mie basah. Di sekitar kampus Universitas YARSI Jakarta, banyak pedagang jajanan yang menggunakan bahan baku tahu dan mie basah, seperti gorengan tahu, tahu krispi, tahu goreng untuk ketoprak, baso tahu dan mie ayam. Tujuan penelitian ini adalah untuk menganalisis kandungan formaldehida pada tahu dan mie basah pada produk pedagang jajanan di sekitar kampus Universitas YARSI Jakarta. Analisis kualitatif adanya formaldehida dalam sampel, dilakukan dengan metode asam kromotropat yang dimodifikasi, dan analisis kuantitatif dengan metode spetrofotometri dengan pereaksi Nash pada 413 nm. Hasil penelitian menunjukkan bahwa semua produk berbahan baku tahu dan mie basah pada pedagang jajanan di sekitar kampus Universitas YARSI Jakarta menggunakan bahan baku tahu dan mie basah yang ditambah bahan pengawet formalin dengan kadar formaldehida dalam tahu berkisar antara (13,9-183,3) ppm dan dalam mie basah berkisar antara (13,9-408,3) ppm.
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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 | 0.002 | 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) | 0.011 | 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".