PREDICTION MODELS WITH MACHINE LEARNING AGAINST STUDENT SUCCESS IN ONLINE LEARNING

ROHNI ENDETTA MASKINI MANIHURUK, ETIS LANDYA BR HOTANG (2021) PREDICTION MODELS WITH MACHINE LEARNING AGAINST STUDENT SUCCESS IN ONLINE LEARNING , SKRIPSI, UNIVERSITAS PRIMA INDONESIA

ABSTRAK

The learning system during the Covid-19 pandemic was carried out online, online learning had both negative and positive impacts. The impact given can affect the success of student learning. The success of learning is the main thing that must be achieved by students. From the success of learning, it can be seen that the online learning process is going well or not. Neural network algorithms are used because they can solve complex problems related to prediction. This research is expected to help lecturers or campus parties to create better online learning. This study using student value data for T.A. 2018/2019 and T.A. 2019/2020, data testing using Rapidminer software, and operator cross validation by applying training cycles of 700, momentum 0.4, learning rate 0.2, and hidden layer 2. Accuracy level The data obtained in the 2018/2019 academic year student value data is 95.55% and the 2019/2020 academic year is 93.17%. From the test results, it was found that the accuracy rate of T.A. 2018/2019 is higher than T.A. 2019/2020, so the success rate in T.A. 2018/2019 before the pandemic is better than the success rate in T.A. 2019/2020 after the pandemic.

JURNAL