Prediction is a systematic estimate that identifies past and future information, we predict student learning success with e-learning based on a log of student activities. In this study, we use the Support vector Machine (SVM) method, which is compared with Particle Swarm Optimization. The problem with this algorithm is that the 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 SVM algorithm. For this reason, attribute selection for existing data is needed, therefore Particle Swarm Optimization (PSO) method is applied for the right attribute selection in determining the success of elearning learning based on student activity logs, because the PSO method can improve accuracy in determining selection of attributes. The SVM algorithm produces an accuracy value of 88.00% and AUC with a value of 0.8120, while with SVM Based on PSO the accuracy value is 88.50% and the AUC value is 0.8460. Therefore, there is an increase from the result of an accuracy value of 0.50% and an AUC value of 3.40%, and then the result is in good classification.
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