Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5
DOI:
https://doi.org/10.37034/jsisfotek.v3i3.54Keywords:
Online Learning, Pandemic COVID-19, Data Mining, C4.5, RulesAbstract
Student learning in schools has changed since the Covid-19 pandemic. Student learning in normal conditions is carried out face-to-face and turns into online or online learning. The research was conducted to predict student learning outcomes during the COVID-19 pandemic so that the results of this study can be used as a reference in policymaking in schools. The C4.5 method was used in the study to classify the data for class XII of the Multimedia Department at SMKN 2 Padang Panjang and the classification results could predict student learning outcomes during the pandemic. Processed student value data were taken from 1 (one) subject as the research data sample. Analysis of the value of student learning outcomes using the C4.5 Method to obtain new knowledge from student learning outcomes data carried out during the COVID-19 pandemic. The data analyzed consisted of attributes of attendance, assignments, daily tests, and test scores which influenced the decision criteria for student learning outcomes in online learning. The learning outcome decision criteria consist of "Satisfactory" and "Not Satisfactory" which refer to the Minimum Completion Criteria. Tests conducted on the training data of learning outcomes show that the value of the Daily Test is the most influential attribute in decision making. Implementation of the results using the RapidMiner Studio 9.2.0 software and produces an accuracy of 83.33% of the test data testing with the rules of data analysis training results. The results of the C4.5 classification testing method in this study can be used to predict student learning outcomes. The test results with an accuracy of 83.33% can be recommended to help schools in making policies
References
Maulana, A., & Fajrin, A. A. (2018). Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma Fp-Growth Pada Data Transaksi Penjualan Spare Part Motor. Klik-Kumpulan Jurnal Ilmu Komputer, 5(1), 27. DOI: https://doi.org/10.20527/klik.v5i1.100 .
Buulolo, Efori. (2020). Data Mining untuk Perguruan Tinggi. Yogyakarta: Deepublish. 6.
Febianto, N. I., & Palasara, N. (2019). Analisa Clustering K-Means Pada Data Informasi Kemiskinan di Jawa Barat Tahun 2018. Jurnal Sisfokom (Sistem Informasi dan Komputer), 8(2), 130. DOI: https://doi.org/10.32736/sisfokom.v8i2.653
Elisa, E. (2017). Analisa dan Penerapan Algoritma C4.5 dalam Data Mining Untuk Mengidentifikasi Faktor-Faktor Penyebab Kecelakaan Kerja Kontruksi PT. Arupadhatu Adisesanti. Jurnal Online Informatika, 2(1). DOI: https://doi.org/10.15575/join.v2i1.71 .
Zhou, F., Xue, L., Yan, Z., & Wen, Y. (2020). Research on College Graduates Employment Prediction Model Based On C4.5 Algorithm. Journal of Physics: Conference Series, 1453. DOI: https://doi.org/10.1088/1742-6596/1453/1/012033 .
Hendrian, S. (2018). Algoritma Klasifikasi Data Mining Untuk Memprediksi Siswa dalam Memperoleh Bantuan Dana Pendidikan. Faktor Exacta, 11(3). DOI: https://doi.org/10.30998/faktorexacta.v11i3.2777 .
David, D. (2019). Penerapan Educational Data Mining Untuk Memprediksi Hasil Belajar Siswa SMAK Ora et Labora. Jurnal Ilmu Komputer, 12(2). DOI: https://doi.org/10.24843/jik.2019.v12.i02.p02 .
Dengen, C. N., Kusrini, K., & Luthfi, E. T. (2020). Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu. Jurnal Ilmiah SISFOTENIKA, 10(1). DOI: https://doi.org/10.30700/jst.v10i1.484 .
Gaol, N. Y. L. (2020). Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5. Jurnal Informasi dan Teknologi, 2(1), 23–29. DOI: https://doi.org/10.37034/jidt.v2i1.22 .
Wang, X., Zhou, C., & Xu, X. (2019). Application of C4.5 Decision Tree for Scholarship Evaluations. Procedia Computer Science, 151, 179–184. DOI: https://doi.org/10.1016/j.procs.2019.04.027 .
Rolansa, F., Yunita, Y., & Suheri, S. (2020). Sistem Prediksi dan Evaluasi Prestasi Akademik Mahasiswa di Program Studi Teknik Informatika Menggunakan Data Mining. Jurnal Pendidikan Informatika dan Sains, 9(1). DOI: https://doi.org/10.31571/saintek.v9i1.1696 .
Li, J., He, J., Liu, Z., Zhang, H., & Zhang, C. (2019). Traffic Accident Analysis Based on C4.5 Algorithm in WEKA. MATEC Web of Conferences, 272. DOI: https://doi.org/10.1051/matecconf/201927201035
