PREDIKSI KINERJA MAHASISWA DALAM PERKULIAHAN DARING BERBASIS E-LEARNING MENGGUNAKAN METODE LOGISTIC REGRESSION

Agustya Nanda Pratiwi; Taghfirul Azhima Yoga Siswa

doi:10.33557/jurnalmatrik.v24i2.1827

Agustya Nanda Pratiwi Universitas Muhammadiyah Kalimantan Timur
Taghfirul Azhima Yoga Siswa

DOI: https://doi.org/10.33557/jurnalmatrik.v24i2.1827

Keywords: Prediction, Student Performance, Online Lectures, Logistic Regression, Accuracy

Abstract

There are many problems that occur in the online learning process, one of which is the difficulty of students in understanding the material well. Various efforts have been declared by lecturers to support online learning, starting from direct material explanations through OpenLearning, Zoom, and Google Meet media. To find out whether the student's performance in this online lecture is good or not. Prediction of student performance in online lectures is used as one of the supports for evaluation decisions at the University of Muhammadiyah, East Kalimantan. The purpose of this study is to determine indicators, implement and evaluate the performance of the Logistic Regression algorithm using the confusion matrix to see student performance in online lectures. The number of data used in this study was 2663 data on odd semester citizenship courses in 2020/2021 and 2021/2022. . The results of the Logistic Regression algorithm using 80% training data sharing and 20% testing data obtained an accuracy value of 91.66%.

References

Dewantara, J. A., & Nurgiansah, T. H. (2021). Efektivitas Pembelajaran Daring di Masa Pandemi COVID 19 Bagi Mahasiswa Universitas PGRI Yogyakarta. Jurnal Basicedu, 5(1), 367–375.

Djusar, S., Sadar, M., & Asril, E. (2021). Analisa Efektifitas Pembelajaran Daring Saat Pandemi Covid-19 Pada Fakultas Ilmu Komputer Universitas Lancang Kuning. Jurnal Sistem Informasi Dan Manajemen (JURSIMA), 9(2), 12–20.

Goldblatt, D. (1994). Analisis Komparasi Algoritma Klasifikasi Data Mining untuk Prediksi Mahasiswa Non Aktif. Seminars in Neurology, 14(1), 241–249.

Handayani, F. (2021). Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network Dalam Prediksi Penyakit Jantung. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 7(3), 329.

Kinoto, J., Damanik, J. L., Situmorang, E. T. S., Siregar, J., & Harahap, M. (2020). Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression. Jurnal Teknologi Dan Ilmu Komputer Prima (JUTIKOMP), 3(2), 503-508.

Prasetyo, R., Nawawi, I., Fauzi, A., Nusa Mandiri, U. (2021). Komparasi Algoritma Logistic Regression dan Random Forest pada Prediksi Cacat Software. Jurnal Teknik Informatika Unika St. Thomas (JTIUST), 06, 2657–1501.

Putra, M. S. T., & Azhar, Y. (2021). Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel. Jurnal Informatika Sunan Kalijaga (JISKA), 6(1), 29–37.

Rini, D. P. (2021). Perbandingan Algoritma Random Forest Classifier , Support Vector Machine dan Logistic Regression Clasifier Pada Masalah High Dimension ( Studi Kasus : Klasifikasi Fake News ). Jurnal Media Informatika Budidarma, 5, 1720–1728.

Santosa, S., & Artanto, F. A. (2015). Prediksi Loyalitas Pelanggan Telekomunikasi Menggunakan Logistic Regression Dengan Seleksi Fitur Particle Swarm Optimizationssion. Jurnal Teknologi Informasi, 11(April), 90–99.

Vinarti, R. A., & Anggraeni, W. (2014). Identifikasi Faktor Prediksi Diagnosis Tingkat Keganasan Kanker Payudara Metode Stepwise Binary Logistic Regression. Jurnal Informatika, 12(2), 70-76.