Analysis of Association Patterns Between Online Gambling Behavior and Divorce in Langkat Regency Using Data Mining
Bibliographic record
Abstract
This study aims to analyze the association patterns between online gambling behavior and the increase in divorce cases in Langkat Regency. The Apriori algorithm in data mining was employed to identify relationships between variables of online gambling behavior such as type of game, frequency, duration, and the amount of money spent, with divorce events. Data were collected through questionnaires distributed to affected respondents and processed using a Python-based bot system. The results indicate a significant correlation between the intensity of online gambling and the increase in divorce rates. The best association rule obtained a support value of 38% with a confidence level of 97.73%, showing that the more frequently a person engages in online gambling with higher spending, the greater the likelihood of divorce. These findings are expected to serve as a reference for local governments and relevant institutions in formulating policies to mitigate the negative impacts of online gambling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".