Linear Regression Algorithm Predicts Bullying Rate Of Sma Negeri 6 Binjai Students
Bibliographic record
Abstract
At SMA Negeri 6 Binjai which is a school located in the city of Binjai in the village of Nangka village, and has several educators and teaching staff in this school, the case study of bullying in schools that is focused in this study, in order to obtain the predicted level of bullying that occurs in SMA Negeri 6 Binjai schools where there is physical and non-physical bullying and through social media. So that the school can find out how much bullying occurs in SMA Negeri 6 Binjai. Method used to solve problems in bullying rate prediction with Linear Regression algorithm. To make it easier for SMA Negeri 6 schools to predict bullying with a linear regression algorithm. The results of testing the linear regression method in the bullying level at SMA Negeri 6 Binjai school used linear regression with the amount of forecasting data that had been analyzed as an analysis, and determined the forecasting results of the value of 19.60222 obtained data results with the number of bullying levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".