Implementation of Mechanical Learning Simple Linear Regression Accuracy Level of Mobile Legend Game Addiction for STMIK Kaputama Students
Bibliographic record
Abstract
This study aims to apply the Simple Linear Regression algorithm in measuring the accuracy of the addiction level of the Mobile Legend game based on the GAS (Game Addict Scale) scale. GAS is a scale used to assess a person's level of gaming addiction, which consists of several scoring items with various indicators of addiction. In this study, data was collected from a group of respondents who had filled out the GAS questionnaire. The value of the GAS scale is used as an independent variable (X) and the level of addiction to the Mobile Legend game is used as a dependent variable (Y). The method used is Simple Linear Regression, where a model will be developed to predict the level of addiction based on the GAS scale. The collected data is divided into two sets: a training set and a test set. The model is built using a training set and then tested using a test set to evaluate its accuracy. The results show that the Simple Linear Regression model is able to provide a fairly accurate prediction of the level of addiction to Mobile Legend games based on the GAS scale. Accuracy evaluations are performed using metrics such as Mean Squared Error (MSE) and R-squared (R²). The evaluation results show that the model has a low MSE value and a high R² value, which indicates that the independent variable (GAS scale) has a significant linear relationship with the dependent variable (Mobile Legend game addiction level). The Simple Linear Regression Algorithm can be used as an effective predictive tool to measure the level of game addiction based on the GAS scale. This research contributes to understanding the relationship between the GAS scale and game addiction, as well as opens up opportunities for further research in developing more complex and accurate prediction models.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".