Analisis Sebaran Tingkat Kriminalitas dan Faktor-Faktor Penyebab di Kota Jayapura
Bibliographic record
Abstract
Crime represents unlawful acts contrary to societal norms. In 2021, Jayapura City experienced a high number of criminal cases, predominantly involving crimes against property and goods. This study maps the distribution of crime rates and identifies contributing factors in Jayapura City using the K-Means Clustering and Analytical Hierarchy Process (AHP) methods. K-Means Clustering analysis revealed five crime levels: high, relatively high, moderate, quite low, and low. North Jayapura District exhibited the highest crime rates among all districts, with Gurabesi Village similarly showing elevated criminal activity. The clustering results were subsequently mapped to visualize the spatial distribution patterns of crime. AHP analysis identified economic factors and low educational attainment as primary contributors to criminal behavior in Jayapura City. Among various intervention alternatives, job creation emerged as the most effective strategy, achieving the highest comparative value for simultaneously improving educational quality and security conditions. These findings provide crucial insights for law enforcement agencies and policymakers to develop targeted crime prevention strategies, focusing on economic development and educational improvement in high-risk areas, particularly North Jayapura District and Gurabesi Village.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".