Implementasi Deep Learning Untuk Rekomendasi Aplikasi E-learning Yang Tepat Untuk Pembelajaran jarak jauh

Abstract

The purpose of this study is to recommend e-learning applications that are appropriate for use in online learning in college environments. The large number of e-learning platforms used by lecturers for online lecture activities results in students being forced to use several e-learning applications depending on the lecturer who teaches the courses taken, for the university also finally gives lecturers policies for distance learning reports each finished giving the material. In this study the data collection method began by taking data from the faculty to find out which e-learning applications were widely used by lecturers, then distributing questionnaires to students and lecturers who used the e-learning application to measure the e-leaning application with the e-learning criteria. Appropriate. The data is then processed into a dataset. The algorithm used in implementing deep learning is Artificial Neural Network (ANN). For the implementation of ANN, 27 variables were determined from the e-learning criteria and 1 target. In this ANN stage, prediction was used with classifications based on preparation, training, learning, evaluation and prediction using the python programming. The results obtained in this study that the Moodle application gets the highest score with an accuracy of 97% to be used as a recommendation for e-learning applications that are appropriate for universities to conduct online lectures.