KAWAL GIZI : PENGELOLAAN BANTUAN PANGAN PEMERINTAH BERBASIS SIG DI LOKUS STUNTING KABUPATEN TASIKMALAYA
Bibliographic record
Abstract
Percepatan penurunan Stunting memerlukan strategi dan metode baru yang lebih kolaboratif dan berkesinambungan mulai dari hulu hingga hilir. Salah satu pembaruan strategi percepatan penurunan Stunting adalah pendekatan keluarga melalui pendampingan keluarga berisiko Stunting untuk mencapai target sasaran, yakni calon pengantin (catin)/ calon pasangan usia subur (PUS), ibu hamil dan ibu menyusui sampai dengan pasca salin, dan anak usia 0-59 bulan. Tim pendamping keluarga akan berperan sebagai ujung tombak percepatan penurunan Stunting. Dalam mendukung kinerja TPK diperlukan sistem informasi yang mendukung pelaporan kinerjanya berupa pengembangan IMAH GIZI yang berbasis Sistem informasi Geografis (SIG) yang dapat memetakan keluarga risiko stunting serta dapat membantu mengelola bantuan pangan pemerintah sehingga tepat sasaran. Kegiatan ini dilaksanakan secara bertahap dalam bentuk peningkatan kapasitas TPK dalam mengelola dan mengolah bantuan pangan pemerintah serta pelaporan kinerja TPK dalam aplikasi tersebut. Hasil menunjukan melalui laporan di aplikasi kawal gizi berbasis SIG di Desa Cikunir terdapat 1 calon pengantin, 12 ibu hamil risiko dan 24 baduta risiko stunting. Melalui data aplikasi berbasis SIG jumlah kelompok risiko stunting yang mendapatkan bantuan social pemerintah adalah 50% ibu hamil risiko dan 37,5% baduta risiko stunting. Pemberian bantuan pangan ini telah memberikan pengaruh terhadap perubahan status gizi keluarga risiko 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".