Application of the K-Means Clustering Method to Cluster Stunting Cases Based on Family Economics in Langkat Regency
Why this work is in the frame
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Bibliographic record
Abstract
Stunting in children is a serious health issue that has long-term impacts on the quality of human resources in Indonesia. Langkat Regency is one of the regions with a high prevalence of stunting. Family economic factors, such as parents' occupation and housing conditions, are suspected to play a significant role in influencing children's nutritional status. However, there is still a lack of data-based studies that specifically cluster stunting cases based on these factors. To address this need, this study applies the K-Means Clustering method to group stunted children based on three main variables: parents' occupation, housing status, and causes of stunting. This algorithm was chosen for its effectiveness in identifying hidden patterns within medium-sized data. The clustering process involved data transformation, determining the number of clusters, calculating distances using Euclidean Distance, and iterative processing to obtain the optimal centroid. The implementation was carried out using MATLAB R2014b software with stunting data obtained from the PPKB-PPA Office of Langkat Regency for the years 2023–2024. The results of the study yielded three main clusters representing the family's economic condition and its relationship to stunting. The patterns found indicate that children from families with unstable jobs and inadequate housing tend to be more vulnerable to stunting. These findings provide a strong foundation for the formulation of more targeted policies in addressing stunting by local governments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it