Penyuluhan Pencegahan Stunting Kepada Peserta Posyandu di RW 04 Kelurahan Kalibaru Kecamatan Cilincing Jakarta Utara
Bibliographic record
Abstract
Kejadian balita stunting merupakan masalah gizi utama yang dihadapi Indonesia. Ratarata prevalensi balita stunting di Indonesia tahun 2015-2017 adalah 36,4%. Persentase balita sangat pendek dan pendek usia 0-59 bulan di Indonesia tahun 2018 adalah 30,8%, Masih banyak bayi usia di bawah 5 tahun (Balita) yang mengalami masalah gizi di DKI Jakarta. Studi Status Gizi Indonesia (SSGI) menyebutkan, sebanyak 20,4% Balita di Jakarta Utara mengalami stunting pada 2021.Artinya, 1 dari 5 Balita di wilayah ini mengalami stunting menurut hasil SSGI 2021. Angka stunting Balita di Jakarta Utara tersebut di atas angka rata-rata stunting Provinsi DKI Jakarta yang sebesar 16,8%. Angka tersebut juga merupakan yang tertinggi dibandingkan dengan 5 wilayah lainnya di Ibu Kota. Penyebab dari Stunting diantaranya adalah faktor dari lingkungan, faktor ibu, faktor pola asuh ibu dan faktor bayi dan balita. Tujuan dari penelitian ini adalah untuk meningkatkan pengetahuan mengenai pencegahan stunting pada ibu hamil dan balita di Kelurahan Kalibaru RT 04 , Cilincing, Jakarta Utara. Penyuluhan dan pencegahan mengenai masalah gizi stunting perlu di sosialisasikan demi kesejahteraan masyarakat.Dari hasil yang didapat bahwa tingkat pengetahuan para peserta bertambah dengan nilai 88,57 % dari hasil pos-test dengan nilai 78,57 %. Hal ini membuktikan bahwa kegiatan penyuluhan ini berhasil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".