Application of the K-Means Algorithm in Traffic Violations In Langkat District (Case Study: Langkat Police)
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
Aktivitas masyarakat berhubungan dengan lalu lintas dan masyarakat lebih memilih menggunakan kendaraan. Rendahnya edukasi serta minim pemahaman tentang peraturan lalu lintas menyebabkan banyak pelanggaran. Meningkatnya jumlah pelanggar lalu lintas menyebabkan meningkatnya data pelanggaran lalu lintas. Banyaknya data pelanggaran lalu lintas menyebabkan terjadinya penumpukan data pada instansi. Maka diperlukan suatu pengolahan data dengan data mining menggunakan Algoritma K-Means. Hasil penelitian diketahui kelompok data pelanggaran lalu lintas yang memiliki kelompok paling tinggi dan paling sering muncul saat diproses yaitu usia 17-25 tahun, dengan kendaraan Honda Vario 150 dan bukti pelanggaran SIM dan STNK. Hasil pengujian 3 cluster dari 502 data pelanggaran diketahui yaitu cluster 1 kelompok data pelanggaran lalu lintas usia 26-45 tahun jenis kendaraan Honda CBR 250 dan bukti pelanggaran SIM dan STNK. Cluster 2 kelompok data pelanggaran lalu lintas usia 26-45 tahun dengan jenis kendaraan Suzuki Nex dengan bukti pelanggaran SIM dan boncengan lebih dari 1. Cluster 3 yaitu kelompok data pelanggaran lalu lintas usia 17-25 tahun, dengan jenis kendaraan Honda Vario 150 dan bukti pelanggaran SIM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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