PERBEDAAN TINGKAT KECUKUPAN NUTRISI DAN PEMBERIAN ASI PADA BALITA STUNTING DAN TIDAK STUNTING DI DESA SUKAMUKTI WILAYAH KERJA UPTD PUSKESMAS JALAKSANA
Bibliographic record
Abstract
Kurangnya asupan zat gizi dapat menyebabkan stunting. Prevalensi stunting di Indonesia 27,67% pada tahun 2019 (Riskesdas, 2019) sedangkan di Kabupaten Kuningan 42% salah satunya Kecamatan Jalaksana Desa Sukamukti terdapat 20 balita sangat pendek dan 115 balita pendek. Penelitian ini bertujuan untuk mengetahui perbedaan tingkat kecukupan nutrisi dan pemberian ASI pada balita stunting dan tidak stunting di Desa Sukamukti Wilayah Kerja UPTD Puskesmas Jalaksana Tahun 2020. Jenis penelitian comparative study dengan desain cross sectional. Populasi 241 balita, menggunakan teknik Proportionate Stratified Random Sampling jumlah sampel yaitu 150 responden. Analisis data menggunakan uji Mann Whitney. Sebagian besar balita memiliki kecukupan nutrisi dalam kategori normal sebanyak 118 responden (78,7%), diberikan ASI secara eksklusif sebanyak 93 responden (62%), tidak stunting sebanyak 79 responden (52,7%). Terdapat perbedaan kecukupan nutrisi (p value = 0,001) dan pemberian ASI (p-value=0,002) pada balita stunting dan tidak stunting. Kesimpulan terdapat perbedaan kecukupan nutrisi dan pemberian ASI pada balita stunting dan tidak stunting, diharapkan dapat meningkatkan pemberian ASI dan porsi makanan yang bergizi supaya tidak terjadi stunting.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".