Predicting Persuasive Message for Changing StudentÕs Attitude using Data Mining - 2017


This paper aims to predict the factors and build prediction models for the persuasive message changing student's attitude by applying classification techniques. We tend to used a questionnaire to gather knowledge like gender, age and their satisfaction with persuasive messages, obtained from students at Khon Kaen University. The classification rule generation process is predicated on the decision tree as a classification methodology where the generated rules are studied and evaluated. We tend to compared the results obtained from 3 algorithms. The results shown that the typical classification correct rate for the ID3 was over the CART and therefore the C4.5 algorithms. The best potency is 98.04%, ninety seven.27percent, and 96.73%, respectively.

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