Online learning is different from offline learning where students meet in the classroom with supervision from the lecturer. Online learning using the Learning Management System (LMS) media requires high awareness from students because their learning activities are not supervised, they are free to study wherever and whenever, so they need to manage and control their own study time without the help of lecturers or administrators. This is one of the causes of the high drop out rate among online learning students, so it is very important for lecturers and administrators to support students in a timely manner to avoid the risk of dropping out. This study uses access log data recorded in the LMS and student statistical information and calculated data, and aims to present a suitable predictive algorithm for drop out early prediction systems for online learning students using machine learning. The results of this study are expected to help online learning organizing institutions to prepare appropriate learning strategies to anticipate dropping out of students. The output of this research is a machine learning model for early prediction of drop out students using machine learning and will be published in several Scopus indexed papers.

Predictions, drop out, online learning, machine learning

• Sucianna Ghadati Rabiha, S.Kom., M.Kom

• Dr. Dina Fitria Murad, S.Kom., M.Kom., CEAA, SMIEEE

• Meta Amalya Dewi, S.Kom,. M.Kom

• Felix Indra Kurniadi, S.Kom., M.Kom.