Application of the K-Means Algorithm in Traffic Violations In Langkat District (Case Study: Langkat Police)
Bibliographic record
Abstract
Societal activities are intertwined with traffic, and people prefer using vehicles. The lack of education and limited understanding of traffic regulations have led to numerous violations. The increasing number of traffic violations has resulted in a rise in traffic violation data. The abundance of traffic violation data has led to data accumulation within institutions. Therefore, data processing through data mining utilizing the K-Means Algorithm is deemed necessary. Research findings have unveiled a cluster of traffic violation data that stands out as the highest and most frequent during processing: the age group of 17 to 25 years, involving Honda Vario 150 vehicles, and evidence of violations related to driver's licenses (SIM) and vehicle registration certificates (STNK). Test results on three clusters from a dataset of 502 traffic violation records reveal the following: Cluster 1 comprises traffic violation data pertaining to individuals aged 26 to 45 years, using Honda CBR 250 vehicles, and violations tied to driver's licenses (SIM) and vehicle registration certificates (STNK). Cluster 2 includes traffic violation data concerning individuals aged 26 to 45 years, utilizing Suzuki Nex vehicles, and violations involving driver's licenses (SIM) as well as carrying more than one passenger. Cluster 3 involves traffic violation data associated with individuals aged 17 to 25 years, employing Honda Vario 150 vehicles, and violations linked to driver's licenses (SIM
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".