PEMETAAN KERAWANAN KRIMINALITAS DI WILAYAH HUKUM KEPOLISIAN RESORT (POLRES) KOTA PAYAKUMBUH TAHUN 2014
Bibliographic record
Abstract
Tujuan penelitian ini untuk: (1) Mengetahui jenis tindak kriminalitas dan persentase setiap jenis tindak kriminalitas yang terjadi di Wilayah Hukum Kepolisian Resort (POLRES) Kota Payakumbuh tahun 2014. (2) Memetakan kerawanan kriminalitas di Wilayah Hukum Kepolisian Resort (POLRES) Kota Payakumbuh tahun 2014.Jenis penelitian deskriptif kuantitatif .Teknik analisis data dilakukan dengan menggunakan metode analisis statistik dan teknik overlay pada peta.Hasil penelitian menemukan bahwa: (1) Jenis tindak kriminalitas meliputi jenis kriminal yaitu: Curas (pencurian dengan kekerasan) sebanyak 88 kasus. Curat (pencurian dengan pemberatan) sebanyak 242 kasus. Curanmor (pencurian sepeda bermotor) sebanyak 87 kasus. Anirat (penganiayaan berat) sebanyak 176 kasus dan Aniring (penganiayaan ringan) sebanyak 160 kasus. Persentase perbandingan antara berbagai jenis kejahatan atau tindak kriminalitas tertinggi sebesar 38,5% untuk Curat di Kecamatan Lampasi Tigo Nagari dan yang paling rendah sebesar 6,1% untuk Curanmor terjadi di Kecamatan Luhak. (2) Kerawanan kriminalitas tertinggi terjadi di Kecamatan Payakumbuh Barat sebesar 49 kali per satuan penduduk dan terendah di Kecamatan Lampasi Tigo Nagari sebesar 4 kali per satuan penduduk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".