Implementasi Metode Binary Decision Tree Support Vector Machine (BDTSVM) untuk Klasifikasi Penyakit Gigi dan Mulut (Studi Kasus: Puskesmas Dinoyo Malang) - Nindy Deka Nivani; Muhammad Tanzil Furqon; Randy Cahya Wihandika (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Teeth and mouth are gates for entry of germs and bacteria that can interfere with health. Complaints against dental and mouth disease are mostly complained by most people in Indonesia, this is corroborated by the fact obtained data from PDGI (Persatuan Dokter Gigi Indonesia) which states that 87% of the people of Indonesia suffer from toothache and among them do not check his teeth to the doctor . Seeing this dentist has an important role in determining the right classification of dental and oral diseases so that patients can immediately treat the disease that is suffering. This research implements the method of Binary Decision Tree Support Vector Machine (BDTSVM) to help classify dental and oral diseases. The Binary Decision Tree method is used to construct binary trees in order to separate classes into two groups, positive and negative. While the Support Vector Machine method is used for the classification process. In this study used 4 kinds of testing that is the test of maximum iteration, lambda parameters, gamma parameters, and complexity parameters. The results obtained from this research is the classification of dental and mouth disease with 6 classes of diseases. Based on the results of the tests that have been done, the average accuracy of 94.28% using the parameter values lambda = 0.5, parameter complexity = 0.1, parameter gamma = 0.01 and maximum iteration = 5