Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

Elin Panca Saputra (Universitas Bina Sarana Informatika)
Sukmawati Angreani Putri (Departement of Information System, STMIK Nusa Mandiri)
Indriyanti Indriyanti (Departement of Information System, STMIK Nusa Mandiri)

Indonesian Journal of Artificial Intelligence and Data Mining
Vol 2, No 1 (2019): March 2019

Prediction is a systematic estimate that identifies past and future information, we predict the success of learning with elearning based on a log of student activities. In our current study we use the Support vector machine (SVM) method which is comparable with Particle Swarm Optimization. It is known that SVM has a very good generalization that can solve a problem. however, some of the attributes in the data can reduce accuracy and add complexity to the Support Vector Machine (SVM) algorithm. It is necessary for existing tribute selection, therefore using the Particle swarm optimization (PSO) method is applied to the right attribute selection in determining the success of elearning learning based on student activity logs, because with the Swarm Optimization (PSO) method can increase accuracy in determining selection of attributes.