Prediction Model of Game Addiction Behavior of Rajabhat University Students with Ensemble Learning Algorithm and Neural Network
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Abstract
This research aimed (1) to study game addiction behavior of Rajabhat University students (2) to create a model and compare the performance of the game addiction behavior prediction model with 6 basic data mining algorithms consisting of neural network, random forest, support vector machine, k-nearest neighbors, decision tree and naïve bayes and (3) to build the model with the ensemble methods 12 algorithm, includes bagging with 6 basic algorithms and boosting with 6 algorithms. The research tools were game addiction screening test (GAST) and RapidMiner Studio 9 program. The sample was students from Phranakhon Si Ayutthaya Rajabhat University. The results revealed that the highest performance model was construction by bagging with neural network model (98.00%), boosting with neural network model (97.60%), respectively. In conclusion, the best model should be applied to develop an application to predict game addiction behavior of Rajabhat University students. In addition, our proposed model is a guideline for finding the cause of game addiction behavior. Surveillance and help people who have problems with game addiction appropriately and develop students' learning to be effective.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in Journal of Industrial Technology Ubon Ratchathani Rajabhat University both hard copy and electronically are belonged to the Journal.
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