Application of the K-Means Clustering Method to Cluster Stunting Cases Based on Family Economics in Langkat Regency
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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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 | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".