Prediction Models with Machine Learning Against Student Success in Online Learning
DOI:
10.33395/sinkron.v6i1.11095Keywords:
Machine Learning, Neural Network, Online Learning, Prediction, Success.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.
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Adi, N. N. S., Oka, D. N., & Wati, N. M. S. (2021). Dampak Positif dan Negatif Pembelajaran Jarak Jauh di Masa Pandemi Covid-19. Journal Ilmiah Pendidikan Dan Pembelajaran, 5(1), 1–13.
Al-Khowarizmi. (2020). Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network. Journal of Informatics and Telecommunication Engineering, 3(2), 258–265. https://doi.org/10.31289/jite.v3i2.3284
Hadianto, N., Novitasari, H. B., & Rahmawati, A. (2019). Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), 163–170. https://doi.org/10.33480/pilar.v15i2.658
Hasibuan, M. S., Nugroho, L. E., & Santosa, P. I. (2019). Model detecting learning styles with artificial neural network. Journal of Technology and Science Education, 9(1), 85–95. https://doi.org/10.3926/jotse.540
Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2017). Prediction of Learner’s Profile Based on Learning Styles in Adaptive E-learning System. International Journal of Emerging Technologies in Learning, 12(6), 31–51. https://doi.org/10.3991/ijet.v12i06.6579
Luvia, Y. S., Hartama, D., Windarto, A. P., & Solikhun. (2016). Penerapan Algoritma C4.5 Untuk Klasifikasi Predikat Keberhasilan Mahasiswa Di Amik Tunas Bangsa. JURASIK (Jurnal Riset Sistem Informasi & Teknik Informatika), 1(1), 75–79. https://doi.org/jurasik.v1i1.12.g10
Mesra, P., Kuntarto, E., & Chan, F. (2021). Faktor–Faktor Yang Mempengaruhi Minat Belajar Siswa di Masa Pandemi. Jurnal Ilmiah Wahana Pendidikan, 7(3), 177–183. https://doi.org/10.5281/zenodo.5037881
Prasetya, T. A., & Harjanto, C. T. (2020). Pengaruh Mutu Pembelajaran Online Dan Tingkat Kepuasan Mahasiswa Terhadap Hasil Belajar Saat Pandemi. Jurnal Pendidikan Teknologi Dan Kejuruan, 17(2), 188–197. https://doi.org/10.23887/jptk-undiksha.v17i2.25286
Pratama, R. E., & Mulyati, S. (2020). Pembelajaran Daring dan Luring pada Masa Pandemi Covid-19. Gagasan Pendidikan Indonesia, 1(2), 49. https://doi.org/10.30870/gpi.v1i2.9405
Puspitorini, F. (2020). Strategi Pembelajaran Di Perguruan Tinggi Pada Masa Pandemi Covid-19. Jurnal Kajian Ilmiah, 1(1), 99–106. https://doi.org/10.31599/jki.v1i1.274
Putra, J. L., & Raharjo, M. (2019). Penerapan Neural Network Dalam Menentukan Tingkat Keberhasilan Immunotherapy. IJCIT (Indonesian Journal on Computer and Information Technology), 4(2), 132–136. https://doi.org/10.31294/ijcit.v4i2.6242
Rahayu, S., Nugraha, F. S., & Shidiq, M. J. (2019). Analisis Tingkat Keberhasilan Cryoterapy Menggunakan Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), 141–148. https://doi.org/10.33480/pilar.v15i2.599
Sismadi, & Kusnadi, Y. (2018). Prediksi Tingkat Kelulusan Siswa Elearning Berbasis Algoritma Fuzzy C-Means. Jurnal TECHNO Nusa Mandiri, 15(1), 1–6.
Supriyatna, A., & Mustika, W. P. (2018). Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 2(2), 152. https://doi.org/10.30645/j-sakti.v2i2.78
Yulianto, L. D., Triayudi, A., & Sholihati, I. D. (2020). Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5. Jurnal Mantik, 4(1), 441–451.
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