Open AccessPredicting the Effect of Violent Gameplaying to Violent Behavior Intention among Females using Tree Regression and AdaBoost Tree Regression
Author(s) -
Maniah Maniah,
Yaya Heryadi,
Agung Trisetyarso,
Bahtiar Saleh Abbas,
Wayan Suparta,
Ford Lumban Gaol
Publication year - 2021
Publication title -
jggag (journal of games, game art, and gamification)
Language(s) - English
Resource type - Journals
ISSN - 2548-480X
DOI - 10.21512/jggag.v4i2.7468
Subject(s) - decision tree , boosting (machine learning) , regression analysis , statistics , regression , adaboost , mean squared error , contrast (vision) , psychology , computer science , machine learning , mathematics , artificial intelligence , support vector machine
The issue on the effect of violent video game to aggressive behavior has gained wide interest from various communities. This paper presents some results of predicting quantitative measure of aggressive behavior from variables that measure violent video game playing. Experiment results showed that Decision Tree Regression (DTR) and Adaptive Boosting Tree Regression (AB-DTR) models predicted aggressive behavior intentions with high accuracy. For predicting Hostile variable: DTR’s training and testing RMSE (0.0, 0.0); AB-DTR’s training and testing RMSE (0.08, 1.08). For predicting Instru variable: DTR’s training and testing RMSE (0.0, 2.18); AB-DTR’s training and testing RMSE (0.0, 3.30) respectively.