Detection of Class Regularity with Support Vector Machine methods

Abstract

One of the most factor that affects the achievement and learning motivation of students is a conducive classroom environment. It can be seen from the student's regularity in the class. Teachers can determine whether the class is adequate or not by monitoring the class condition through video. The research tries to apply the extraction of imagery and sound features by using the Centroid extraction method and the MFCC along with classifying the regular or irregular classrooms with the SVM methods which are taken by video installed in a classroom. The video will be split into image data and sound data. The process of image data starts with reading the input, then it goes to the stages of preprocessing, segmentation with K-Means, morphology, and the most important part is to get information before it is classified by the SVM method to get its class regularity. The sound frequency will be extracted by the MFCC method and then it is classified by the SVM method to get the class noise. The results of this research get an accuracy value of 78% in the linear kernel and 70% in the polynomial kernel. This research uses 50 test data consisting of 25 regular data and 25 irregular data taken directly through video recording. These results prove that the SVM method has given good classification results for regular and irregular classes.