IMPLEMENTASI METODE K-MEANS CLUSTERING DALAM PENGELOMPOKAN PENYEBARAN COVID-19 DI SURABAYA
Bibliographic record
Abstract
COVID-19 is an infection or spread of the CORONA virus. The spread of the Corona Virus in Indonesia itself includes a fairly fast spread due to the way it is spread which is quite easy. The impact of the COVID-19 pandemic can still be felt today. The spread of COVID-19 that is evenly distributed in various provinces in Indonesia makes it difficult to handle and overcome it, therefore a grouping based on regions in Indonesia is needed. This grouping will produce a focal point for the spread of COVID-19 in various regions. This study uses the K-Means Clustering method to group data on the spread of COVID-19. This study tested the number of clusters using the Silhouette Index method to find out the optimal number of clusters of 2,3, 4, and 5 clusters. The results of the trial of the number of clusters in grouping the data on the spread of COVID-19 in each kelurahan in Surabaya using the K-Means Clustering method resulted in a good structure in the 3, 4, and 5 cluster trials, while the 2 cluster trial resulted in a strong structure with Silhouette. The index is 0.8021.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".