Development of an Android-Based Application to Prevent Baby-Born Stunted
Bibliographic record
Abstract
INTRODUCTION: Stunting is still a problem for infants under five years old in Indonesia. One solution to improve the health status of pregnant women to prevent the child born stunting in the digital era is to design an application called ACALS, the “Aplikasi Cegah Anak Lahir Stunting” (ACALS)/ Application for preventing the birth of children with stunting. The application was created as an innovation and strategy for preventing and handling stunting. This study aims to evaluate the design and structure of ACALS as an application used to support the optimization of antenatal care for pregnant women. METHODOLOGY: The research design is Research and Development to evaluate the quality of innovative products in applications with user authority for pregnant women. This research was conducted in 2019 at the Jonggol Community Health Center, Bogor Regency, West Java, Indonesia. Through this research, a smartphone-based application called the ACALS/ Application was designed to prevent the birth of children with stunting, which was developed based on the Ministry of Health's MCH Handbook. RESULT: The application is considered high quality based on its evaluation of all dimensions, which include completeness, correctness, security, timeliness, benefits, efficiency, reliability, and usability. Therefore, the design and structure of ACALS have been significantly developed. CONCLUSION: It was concluded that ACALS is very beneficial for pregnant women during pregnancy as a means of monitoring and supporting the optimization of antenatal care to prevent the birth of stunted babies.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".