Detection of Class Regularity with Support Vector Machine methods - Rosiana Dewi, Ni Wayan Emmy; Aris Gunadi, I Gede; Indrawan, Gede (Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 2020)
One 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 study attempted to implement the extraction of imagery and sound features using the Centroid extraction method and the MFCC and classify the regular or irregular classrooms with the SVM methods taken via 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 goes to the stages of preprocessing, segmentation, morphology, and the most important part is extraction 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 classified by the SVM method to get the class noise. The results of this study get an accuracy value of 78% in the linear kernel and 70% in the polynomial kernel for the test data of 50. These results prove that the class regularity detection system with the SVM method provides good classification results for regular and irregular students.