ANALISIS CLUSTERING STUNTING DENGAN DISTANCE EUCLID
Bibliographic record
Abstract
Entering the Industrial Revolution Era 4.0, human resources must be supported by healthy and intelligent human resources so that they can increase competitiveness. The world still faces the problem of hunger and malnutrition today. According to a Unicef report, many people suffer from malnutrition in the world. The World Health Organization (WHO) says that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster stunting so as to produce patterns that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid, the Euclid algorithm is able to cluster stunting prevalence data into 3 clusters with a little category of 66%, a medium category of 28%, a lot of category of 6%. The results of the classification and clustering of the best stunting prevalence in cluster two with a small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide patterns for handling and optimizing stunting. in each district/city. Malnutrition is estimated to be the main cause of 3.1 million child deaths every year. Therefore, efforts need to be made to minimize stunting by predicting stunting sufferers. The prediction results can be used as an early prevention effort.
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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 | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".