Analysis of the Pattern of the Relationship the Intensity of Playing Onilne Games and Learning Interest Using Association Rule Mining (Apriori) at STMIK KAPUTAMA
Bibliographic record
Abstract
The rapid development of information technology has a significant impact on the learning lives of students, one of which is through the increasing intensity of playing online games. This phenomenon raises concerns regarding its influence on learning interests, so it is necessary to conduct an in-depth analysis to see the pattern of relationships that occur. This study aims to analyze the relationship between the intensity of playing online games and the learning interest of STMIK Kaputama students using the Association Rule Mining method with a priori algorithm. The research data was obtained through questionnaires that were shared with students, then processed into binary tabular forms so that they could be processed using the RapidMiner software. The analysis process is carried out through the stage of forming frequent itemset, calculating support and confidence, to finding association rules that meet the minimum requirements. The results showed that there were several significant relationship patterns between the variables of the intensity of playing online games and learning interest. For example, the pattern "PS1 & WBS4 & TKG2 & UWB1" has support of 35% and results in a confidence value that shows a strong association between playing time factors, dependency levels, and learning efforts. In general, the higher the intensity of playing online games, the more it affects the decrease in students' interest in learning. These findings can be an input for the campus and students in managing gaming activities so that they do not have a negative impact on academics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".