Prediction of Criminal Theft Locations at the Binjai Police Station using Historical Data and the KNN Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to predict the locations of theft crimes in the Binjai Police Department by utilizing historical data obtained from Binjai Police. The implementation of the K-Nearest Neighbor (KNN) algorithm was carried out to analyze crime patterns based on variables such as district location, time of occurrence, day of occurrence, and type of crime. The historical data were processed through normalization, splitting into training and testing datasets, and model evaluation using RapidMiner software. The results show that the KNN algorithm is able to classify with a fairly good level of accuracy, making it a useful basis for providing predictive information on theft-prone locations. These findings are expected to assist the police and the Binjai city government in formulating crime prevention strategies and increasing public awareness.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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