Main Article Content
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.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Jurnal Lontar Komputer as publisher of the journal. Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, as well as translations. The reproduction of any part of this journal (printed or online) will be allowed only with a written permission from Jurnal Lontar Komputer. The Editorial Board of Jurnal Lontar Komputer make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal.
This work is licensed under a Creative Commons Attribution 4.0 International License.
 I. Gede Aris Gunadi, A. Harjoko, R. Wardoyo, and N. Ramdhani, “Fake Smile Detection Using Linear Support Vector Machine,” in Proceedings of 2015 International Conference on Data and Software Engineering, ICODSE 2015, pp. 103–107, 2016.
 R. Munawarah, O. Soesanto, and M. R. Faisal, “Penerapan Metode Support Vector Machine Pada Diagnosa Hepatitis,” Kumpulan JurnaL Ilmu Komputer (KLIK), vol. 04, no. 01, pp. 73–82, Feb 2016.
 T. Ozeki and E. Watanabe, “Analysis of the Behavior of Students Considering Privacy,” in The 6th IIEEJ International Conference on Image Electronics and Visual Computing, no. 1P-3, 2019.
 I. M. S. P. Kadek Novar Setiawan, “Klasifikasi Citra Mammogram Menggunakan Metode K-Means, GLCM, dan Support Vector Machine (SVM),” Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), vol. 6, no. 1, pp. 13–24, 2018.
 A. Awais, S. Kun, Y. Yu, S. Hayat, A. Ahmed, and T. Tu, “Speaker Recognition Using Mel Frequency Cepstral Coefficient and Locality Sensitive Hashing,” in 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018, pp. 271–276, 2018.
 B. J. Mohan and N. Ramesh Babu, “Speech Recognition Using MFCC and DTW,” in 2014 International Conference on Advances in Electrical Engineering, ICAEE 2014, pp.1-4, 2014.
 O. Lézoray and L. Grady, Image Processing and Analysis with Graphs: Theory and Practice. CRC Press, 2012.
 M. Loesdau, S. Chabrier, and A. Gabillon, “Hue and saturation in the RGB color space,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 203–212, Springer International Publishing, 2014.
 E. S. Gedraite and M. Hadad, “Investigation on the effect of a Gaussian Blur in image filtering and segmentation,” in Proceedings Elmar - International Symposium Electronics in Marine, pp. 393–396, 2011.
 Darma Putra, Pengolahan Citra Digital. Yogyakarta: Penerbit Andi, 2010.
 S. S. Dhumal and S. S. Agrawal, “MRI Classification and Segmentation of Cervical Cancer to Find the Area of Tumor,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 3, no. VII, pp. 21–26, 2015.
 A. Mohd, G. K. Ram, and A. Shafeeq, “Skin Cancer Classification Using K-Means Clustering,” International Journal of Technical Research and Applications, vol. 5, no. 1, pp. 62–65, 2017.
 H. Kim, E. Ahn, S. Cho, M. Shin, and S. H. Sim, “Comparative Analysis of Image Binarization Methods for Crack Identification in Concrete Structures,” Cement and Concrete Research, vol. 99, pp. 53–61, Sep. 2017.
 L. Najman, J. C. Pesquet, and H. Talbot, “When Convex Analysis Meets Mathematical Morphology on Graphs,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9082, pp. 473–484, 2015.
 Y. Chugh, R. Gupta, and R. Kaushik, “Image Enhancement Using Morphological Operators,” International Journal of Engineering Technology, vol. 3, special Issue, pp. 61–66, 2015.
 T. Chamidy, “Metode Mel Frequency Cepstral Coeffisients (MFCC) pada Klasifikasi Hidden Markov Model (HMM) untuk Kata Arabic pada Penutur Indonesia,” Jurnal Matics, vol. 8, no. 1, pp. 33-40, 2016.
 N. Guenther and M. Schonlau, “Support Vector Machines,” The Stata Journal: Promoting Communications on Statistics and Stata, vol. 16, no. 4, pp. 119-127, 2016.
 M. Aykanat, Ö. Kılıç, B. Kurt, and S. Saryal, “Classification of Lung Sounds Using Convolutional Neural Networks,” Eurasip Journal on Image and Video Processing, no. 65, 2017.
 A. F. Indriani and M. A. Muslim, “SVM Optimization Based on PSO and AdaBoost to Increasing Accuracy of CKD Diagnosis,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 10, no. 2, pp. 119-127, Aug 2019.
 Y. R. Nugraha, A. P. Wibawa, and I. A. E. Zaeni, “Particle Swarm Optimization-Support Vector Machine (PSO-SVM) Algorithm for Journal Rank Classification,” in Proceedings - 2019 2nd International Conference of Computer and Informatics Engineering: Artificial Intelligence Roles in Industrial Revolution 4.0, IC2IE 2019, 2019, pp. 69–73.
 P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum Support Vector Machine for Big Data Classification,” Physical Review Letters, vol. 113, no. 13, pp. 130503, Sep. 2014.
 D. P. Kaucha, P. W. C. Prasad, A. Alsadoon, A. Elchouemi, and S. Sreedharan, “Early Detection of Lung Cancer using SVM Classifier in Biomedical Image Processing,”in IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 3143–3148, 2017.
 R. Fernandes de Mello, M. Antonelli Ponti, R. Fernandes de Mello, and M. Antonelli Ponti, “Introduction to Support Vector Machines,” in Machine Learning, 2018.
 M. Gönen and E. Alpaydin, “Multiple Kernel Learning Algorithms,” Journal of Machine Learning Research, vol. 12. pp. 2211–2268, Jul-2011.
 S. Pahwa and D. Sinwar, “Comparison Of Various Kernels Of Support Vector Machine,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 3, no. VII, pp. 532–536, 2015.