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Title: A Comparison of Efficiency in Classifying Gaming Data using Data Mining Techniques
Authors: Hmeanaui, Thiraporn
Pechkonchom, Taveesa
Lukkananuruk, Nitima
Hengpraphorm, Kairung
Hengpraphorm, Supojn
Keywords: Feature
Decision Tree
Issue Date: 10-Jul-2020
Abstract: The aim of this research is to compare the efficiency of the suitable technique for gaming data classification. In the experiment, Weka 3.8.4 software is used with two data classification techniques: J48 and JRip to test with 3,196 gaming data with 37 features. The experimental results show that J48 gives the best result of the classification accuracy (99.43%). The second technique is JRip with the classification accuracy = 99.19% respectively. Therefore, J48 is the suitable technique for gaming data classification.
Appears in Collections:Proceedings of the 12th NPRU National Academic Conference

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