Forecasting System For Increasing Crime At The Binjai City Police Station With The Application Of The Website-Based Exponential Smoothing Method
Bibliographic record
Abstract
Binjai City is the closest city to the city of Medan, which is the heart of North Sumatra. As one of the closest cities to the city gate of Medan, there are many activities that require a lot of activities. In Binjai City there is a Binjai City Police Office which is located Jl. Sultan Hasanuddin No.1, Binjai City, North Sumatra. Based on crime data at the Binjai City Police Station, it shows that crime that occurs every day is constantly increasing. This is because several factors influence it, namely internal factors within oneself such as having a realistic mindset and so on while external factors such as economic level factors or low education levels that have an impact on the difficulty of finding jobs, uneven population density, very minimal salaries, urgent needs, supportive environmental situations, social inequality trigger envy and resentment, environmental associations that require costs and so on. When people are faced with such a situation, the thing that will come to their mind is how to get money to meet the needs of their families at all costs. So that it affects one's mentality and actions to commit criminal acts.Exponential smoothing method is a procedure that continuously improves forecasting with the average (smoothing = smoothing) past values of a time sequence data with. The Exponential smoothing method is a development of the Moving Averages method.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".