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The Developed of Models to Forecast Aptitude of Learning by 4 MAT theory Using the Decision Tree Analysis |
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| รหัสดีโอไอ | |
| Creator | Kanittha Deesubin |
| Title | The Developed of Models to Forecast Aptitude of Learning by 4 MAT theory Using the Decision Tree Analysis |
| Publisher | Walailak University |
| Publication Year | 2560 |
| Journal Title | Journal of Learning Innovations Walailak University |
| Journal Vol. | 3 |
| Journal No. | 1 |
| Page no. | 45-58 |
| Keyword | Aptitude to learn, Decision Tree, 4 MAT Theory |
| URL Website | https://www.tci-thaijo.org/index.php/jliwu |
| Website title | Journal of Learning Innovations Walailak University |
| ISSN | 2408-2481 |
| Abstract | The objective of this research was to forecast the aptitude of learning by 4 MAT theory using the Decision Tree Analysis. To build and test the model predictions, the researcher used WEKA on modeling techniques with a decision tree. The researcher used the method of classification and learning methods by J48 (algorithm C4.5 Version 8.0) to learn from the Train Dataset and the modeling of trees to be used for the classification of information as the algorithm web continues to evolve as a module on the network. The resulting model will be available in the form of rules of classification of learning with the training set, which will be testedby the test data by means of the k-fold cross-validation and percentage split. The results showed that forecast aptitude of learning by 4 MAT theory using Decision Tree the analysis, developed with the training set and the test data set resulted in higher performance than the model developed in other ways with corrected 77.65 percent accuracy at 77.65 percent, memory at 77.65 percent and balance at 77.40 percent. This shows that the training set and the test set can be used to develop theforecast on aptitude of learning by 4 MAT theory using the separate decision tree with accuracy and precision in the forecastingthe aptitude of learning by 4 MAT theory. |
