Penerapan Metode Clustering pada Status Gizi Ibu Hamil
Bibliographic record
Abstract
Pregnancy is a process in a woman's life, where major changes occur in her physical, mental and social aspects. These changes cannot be separated from the factors that influence them, namely physical factors, psychological factors and environmental, social, cultural and economic factors. One of the nutritional problems of pregnant women is chronic energy deficiency (KEK). Chronic energy deficiency (KEK) is a nutritional problem caused by a lack of food intake over a long period of time, a matter of years. Datar City Health Center is one of the agencies that provides health services for the local community and helps resolve problems with the health and nutritional development of mothers and children to prevent problems with malnutrition in pregnant women. The aim of the research is to make it easier for agencies to manage data and obtain complete information about the nutritional status of pregnant women. From 20 data, 3 groups were obtained, Cluster 1 had 4 data on the nutritional status of pregnant women, Cluster 2 had 4 data on the nutritional status of pregnant women and Cluster 3 had 12 data on the nutritional status of pregnant women. And the largest group obtained was cluster 3 with the data group on the nutritional status of pregnant women found in the gestational age group (X), namely 14-27 weeks old, with screening results (Y) namely adequate nutrition, and the causal factors (Z) that occurred were economic factors
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".