Grouping of Flood Victim Data Based on Damage Rate Using K-Means Algorithm Case Study: Binjai City Social Service
Bibliographic record
Abstract
This study aims to classify flood disaster victim data in Binjai City based on three main variables: sub-district or location, damage level, and type of aid received. The data were obtained from the Binjai City Social Service in 2024 and processed using the K-Means Clustering method with the Matlab R2014b application. The stages include data transformation, determining the number of clusters, selecting initial centroids, calculating Euclidean distance, and evaluating the results. Tests were conducted with configurations of 2, 3, 4, and 5 clusters. In the 2-cluster configuration, the distinction was observed between areas with low damage and limited to moderate aid, and areas with medium damage and more extensive aid. In the 3-cluster configuration, the second test produced the most optimal cluster in Kartini Sub-district with light damage and limited food aid. In the 4-cluster configuration, the most compact cluster was found in Setia Sub-district with medium damage and aid in the form of food and blankets. In the 5-cluster configuration, the most specific result was obtained in Rambung Barat Sub-district with medium damage and aid in the form of food and blankets. These findings indicate that the 5-cluster configuration provides more detailed and targeted classification, serving as a strategic reference for aid distribution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".