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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Br Hotang, Etis Landya
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Computer Science & IT

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Model Prediksi Dengan Machine Learning Terhadap Keberhasilan Mahasiswa Dalam Pembelajaran Online Yennimar; Manihuruk, Rohni Endetta; Br Hotang, Etis Landya
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11095

Abstract

The learning system during the Covid-19 pandemic was carried out online, online learning had a negative impact and a positive impact. 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. To determine the success rate of online learning, testing is carried out by applying a neural network algorithm. 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. In this study using student grade data for Academic Year 2018/2019 and Academic Year 2019/2020, data testing using Rapidminer software and operator cross validation. In testing the Academic Year 2018/2019 and Academic Year 2019/2020 value data using 700 training cycles, 0.4 momentum, 0.2 learning rate and hidden layer 2. The level of accuracy obtained in the 2018/2019 student grade data is 95, 55% and Academic Year 2019/2020 which is 93.17%. From the test results, it was found that the accuracy rate of Academic Year 2018/2019 is higher than Academic Year 2019/2020, so the success rate in Academic Year 2018/2019 before the pandemic is better than the success rate in Academic Year 2019/2020 after the pandemic.
Co-Authors Manihuruk, Rohni Endetta Yennimar