Inovasi Aplikasi Adhimukti Brata Care Stunting Berbasis AppSheet Untuk Pendataan Status Gizi Anak di Kabupaten Banggai
Bibliographic record
Abstract
Stunting merupakan masalah kesehatan serius di Indonesia, termasuk di Kabupaten Banggai dengan prevalensi 29,1% pada tahun 2023. Keterlambatan intervensi sering terjadi akibat keterbatasan data gizi anak yang akurat, terbaru, dan mudah diakses. Penelitian ini bertujuan mendeskripsikan pengembangan Aplikasi Adhimukti Brata Care Stunting berbasis AppSheet sebagai solusi pendataan gizi anak secara real-time. Metode yang digunakan adalah research and development (R&D) dengan model prototyping, meliputi tahap analisis kebutuhan, perancangan, implementasi, uji coba teknis, dan penyempurnaan. Data diperoleh dari hasil uji coba aplikasi, observasi proses input, serta masukan pengguna awal yaitu petugas DP2KBP3A, PKB, dan kader posyandu. Hasil pengembangan menunjukkan aplikasi mampu mempercepat proses input dan monitoring data, mengurangi kesalahan pencatatan, serta mempermudah akses informasi untuk pengambilan keputusan. Perbandingan kondisi sebelum dan sesudah implementasi menunjukkan adanya peningkatan pada kecepatan pelaporan, akurasi data, dan kemudahan monitoring. Disimpulkan bahwa aplikasi ini dapat memperkuat sistem pendataan gizi anak dan mendukung respons lebih cepat dalam penanganan stunting di tingkat daerah, dengan catatan perlu adanya pelatihan berkelanjutan, dukungan regulasi, dan peningkatan kapasitas SDM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".