Klasifikasi Penyakit Gigi Dan Mulut Menggunakan Metode Support Vector Machine - Ana Mariyam Puspitasari; Dian Eka Ratnawati; Agus Wahyu Widodo (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Oral diseases is one of the most serious diseases that impact to human health in general, as the mouth is a place where the germ and bacteria oral diseases should be handled immediately but not all dental expert can quickly do the handling due to the lack of a dental expert that is available in the hospital for 24 hours. Knowing the types oral diseases since the beginning is very important. Therefore, a system that has the ability to classify types of oral diseases will be very helpful in order to help the community in conducting early diagnosis of oral diseases. This research used classification system using of SVM method because SVM method can resolve the problem of classification and regression with linear or non linear kernel with its capability as a learning algorithm on the classification or regression. This research used One-Againts-All strategies for non linear process and used RBF kernel. The results obtained using SVM method has a mean median values of accuracy - 94,442% using the dataset as much as 122 data and with the parameter Î» value SVM training sequential (lamda) = 0.1, y (gamma) = 0.1, C (Complexity) = 1, Îµ (epsilon) = 1.10-10 with itermax = 50 and ratio data 80%: 20%. The results shows good accuracy, and the research can be applied to help perform classification of oral disease using support vector machine method.