Massive Open Online Courses (MOOCs) Recommendation Modeling using Deep Learning

Since knowledge in the world of internet has always been developed with updated information. Recommendation system for a Massive Open Online Courses (MOOCs) can help create endless learning opportunities. This study presents a Massive Open Online Courses Recommendation Modeling using Deep Learning with Multilayer Perceptron architecture which is suitable for enormous data analysis. The research methodology begins with the process used for the data analysis process, using the data mining technique according to the Cross-industry standard process for data mining (CRISP-DM), consisting of six steps: business understanding, understanding of data, data preparation, modeling, evaluation and deployment. We received a set of data from Harvard and MIT, published for edX learning data in 2012-2013, consisting of 16 programs, 18 features and 641138 sample items. The research found that the most appropriate model is a model with 7 hidden layers and 1e-3 learning rate, processed by GPU acceleration for 250 Epochs. The evaluation of the model's performance is evaluated by calculating the precision value using 542784 testing samples.