IMPLEMENTASI METODE K-MEANS CLUSTERING DALAM PENGELOMPOKAN PENYEBARAN COVID-19 DI SURABAYA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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