Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis

• Main Authors: Kismiantini, Kismiantini, Shazlyn M. Shaharudin, Shazlyn M. Shaharudin, Adi Setiawan, Adi Setiawan, Rasyidhani Aditya Rizky, Rasyidhani Aditya Rizky, Salsa-Billa Syahida Al-Hasania, Salsa-Billa Syahida Al-Hasania, Murugan Rajoo, Murugan Rajoo, Hairulnizam Mahdin, Hairulnizam Mahdin, Salama A Mostafa, Salama A Mostafa
• Format: Article
• Language: English
• Published: ASPG
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/10624/1/J16631_fb66c29b2d8fa4d1997854a1a1d8eabf.pdf; http://eprints.uthm.edu.my/10624/; https://doi.org/10.54216/FPA.130106

The COVID-19 pandemic is a virus that is changing habits in human life worldwide. The COVID19 outbreaks in Indonesia have forced educational activities such as teaching and learning to be conducted online. Teaching and learning activities using the online method are familiar, but the effectiveness of this method still needs to be investigated to be applied in all educational systems. This study used the predictive modeling of Recurrent Forecasting (RF) derived from Singular Spectrum Analysis (SSA) to know the online learning method's practicality on the student's academic performance. The fundamental notion of the predictive fusion model is to improve the effectiveness of several forms of forecast models in SSA by employing a fusion method of two parameters, a window length (L), and a number of leading components (r). This study used undergraduate students' grade point averages (GPA) from a public university in Indonesia through online classes during the COVID-19 epidemic. The experiments unveiled that a parameter of L = 14 (