Open AccessDETEKSI PENYALAHGUNAAN NARKOBA DENGAN METODE TWIN BOUNDED SVM
Author(s) -
Berny Pebo Tomasouw,
Yopi Andry Lesnussa
Publication year - 2021
Publication title -
barekeng
Language(s) - English
Resource type - Journals
eISSN - 2615-3017
pISSN - 1978-7227
DOI - 10.30598/barekengvol15iss4pp753-760
Subject(s) - support vector machine , pattern recognition (psychology) , kernel (algebra) , artificial intelligence , scheme (mathematics) , nonlinear system , bounded function , computer science , machine learning , mathematics , mathematical analysis , physics , combinatorics , quantum mechanics
Twin Bounded SVM (TB-SVM) is an improvement of the Twin SVM method and has advantages in classification problems compared to standard SVM. In this research, linear TB-SVM and nonlinear TB-SVM methods will be applied to detect drug use based on 23 symptoms experienced. The training and testing data is divided into three partition data schemes (60/40 scheme, 70/30 scheme and 80/20 scheme) in order to determine the best level of accuracy that can be obtained. The test results show that the nonlinear TB-SVM with the RBF kernel has a better accuracy rate than the linear TB-SVM, that is 80% at 60/40 scheme, 90% at 70/30 scheme, and 95% at 80/20 scheme.