Application of Data Mining in Analyzing the Effect of Parents' Employment and Education Level on Student Behavior Using the A PRIORI Method (Case Study: SDN 024769 Binjai)
Bibliographic record
Abstract
Behavior is a person's reaction to a stimulus that comes from the external environment. Parents are one of the main factors in the formation of children's behavior. This study aims to find out the effect of parents' work and education on student behavior. By using RapidMiner in testing 234 SDN 024769 Binjai student data, using the Apriori method and setting a minimum support value of 8% and 70% confidence, 1207 rules were obtained in the entire set and 2 rules in 9 itemsets. And the best rule with the highest value is obtained, if the father's job is self-employed, the mother's job is self-employed, the father's last education is high school, the mother's last education is high school, the time the father spends working is more than 8 hours per day, the time the mother spends working is more than 8 hours per day, the time the father spends on family is every day, and the time the mother spends on family is every day then the student has good behavior at school, with a support value of 8.5% and a certainty value of 95.2%.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".