Application of the Apriori Algorithm to Analyze the Correlation of Underage Marriage Factors
Bibliographic record
Abstract
This study aims to analyze the correlation between various factors contributing to underage marriage in Kota Binjai. The primary variables influencing underage marriage include economic, social, cultural, parental, and educational factors. These five variables are considered significant in the prevalence of underage marriage within the Kota Binjai community. The methodology employed involves data mining techniques using the Apriori algorithm to identify the most frequently occurring data correlations. The analysis reveals that the best rule with 2 itemsets has a support of 42.7% and a confidence of 69.6%, while the best rule with 3 itemsets has a support of 26.7% and a confidence of 71.4%. For 4 itemsets, the support is 12% with a confidence of 50%. These findings indicate that economic factors are the most frequently appearing and consistently influential in the decision of Kota Binjai residents to engage in underage marriage, both independently and in combination with social, cultural, and parental influence factors
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".