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Fusion: Practice and Applications

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https://doi.org/10.54216/FPA

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2692-4048ISSN (Online) 2770-0070ISSN (Print)
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Fusion: Practice and Applications

Volume 13 , Issue 1 , PP: 79-88, 2023 | Cite this article as | XML | PDF | Full Length Article

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

Kismiantini 1 * , Shazlyn M. Shaharudin 2 , Adi Setiawan 3 , Rasyidhani Aditya Rizky 4 , Salsa-Billa Syahida Al-Hasania 5 , Murugan Rajoo 6 , Hairulnizam Mahdin 7 , Salama A Mostafa 8

  • 1 Universitas Negeri Yogyakarta, Indonesia - (kismi@uny.ac.id)
  • 2 Universiti Pendidikan Sultan Idris, Malaysi - (shazlyn@fsmt.upsi.edu.my)
  • 3 Universitas Negeri Yogyakarta, Indonesia - (adi.setiawan@gmail.com)
  • 4 Universitas Negeri Yogyakarta, Indonesia - (rasyidhaniadityarizky@gmail.com)
  • 5 Universitas Negeri Yogyakarta, Indonesia - (salsabillasyahida@gmail.com)
  • 6 Universiti Pendidikan Sultan Idris, Malaysia - (murugan@fsmt.upsi.edu.my)
  • 7 Universiti Tun Hussein Onn Malaysia, Malaysia - (hairuln@uthm.edu.my)
  • 8 Universiti Tun Hussein Onn Malaysia, Malaysia - (salama@uthm.edu.my)

    • Doi: https://doi.org/10.54216/FPA.130106

    Received: March 12, 2023 Revised: June 19, 2023 Accepted: August 23, 2023
    Abstract

    The COVID-19 pandemic is a virus that is changing habits in human life worldwide. The COVID-19 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 ( ) yielded the finest prediction using the RF-SSA model with a root mean square error (RMSE) value of 0.20. Such a finding signified the ability of the RF-SSA to project the students' academic performance according to the GPA for the forthcoming semester. Nonetheless, developing the RF-SSA algorithm for greater effectiveness is essential to acquiring more datasets, such as by gathering a bigger group of respondents from several Indonesian universities.

    Keywords :

    Covid-19 , RF-SSA , forecasting , GPA , SSA.

    References

    [1] World Health Organization. (2020). Virtual press conference on COVID-19. Osteoarthritis and Cartilage.

    [2] Menteri Pendidikan dan Kebudayaan Republik Indonesia. (2020). Surat Edaran Nomor 4 Tahun 2020 Tentang Pelaksanaan Kebijakan Pendidikan Dalam Masa Darurat Penyebaran Coronavirus Disease (COVID-19). Republik Indonesia.

    [3] Samsudin, N. a. M., Shaharudin, S. M., Sulaiman, N. a. F., Smail, S. I., Mohamed, N. S., & Husin, N. H. M. (2022). Prediction of Student‘s Academic Performance during Online Learning Based on Regression in Support Vector Machine. International Journal of Information and Education Technology, 12(12), 1431–1435. https://doi.org/10.18178/ijiet.2022.12.12.1768

    [4] UNY. (2020). INSTRUKSI REKTOR NO-1 TH 2020 COVID-19.pdf. Retrieved from https://www.uny.ac.id/sites/www.uny.ac.id/files/INSTRUKSI REKTOR NO-1 TH 2020 COVIS-19.pdf.

    [5] Zulfikri, M., Shaharudin, S. M., Rajak, N. A., & Khan, I. A. (2021). Predictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model. International Journal of Biology and Biomedical Engineering, 15, 184–189. https://doi.org/10.46300/91011.2021.15.21

    [6] Burman, I., & Som, S. (2019). Predicting students academic performance using support vector machine. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 756–759. IEEE.

    [7] Kanneganti, A., Sia, C. H., Ashokka, B., & Ooi, S. B. S. (2020). Continuing medical education during a pandemic: An academic institution’s experience. Postgraduate Medical Journal, 384–386.

    [8] Fitri, F., Gamayanti, N. F., & Gunawan, G. (2017). Metode SSA pada data produksi perikanan tangkap di Provinsi Jawa Barat. Jurnal Ilmiah Matematika dan Pendidikan Matematika, 9(2), 95.

    [9] S. M. Shaharudin, N. Ahmad, N. F. F. Mohamed, and N. Aziz, “Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data,” International Journal on Advanced Science, Engineering and Information Technology, Aug. 2020, doi: 10.18517/ijaseit.10.4.11653.

    [10] Sandhu, P., & de Wolf, M. (2020). The impact of COVID-19 on the undergraduate medical curriculum. Medical Education Online, 25(1), 20–22. Taylor & Francis. Retrieved from https://doi.org/10.1080/10872981.2020.1764740

    [11] S. M. Shaharudin, S. Ismail, M. A. Samsudin, A. Azid, M. L. Tan, and M. C. Basri, “Prediction of Epidemic Trends in COVID-19 with Mann-Kendall and Recurrent Forecasting-Singular Spectrum Analysis,” Sains Malaysiana, vol. 50, no. 4, pp. 1131–1142, Apr. 2021, doi: 10.17576/jsm-2021-5004-23.

    [12] S. M. Shaharudin, S. Ismail, N. H. Hassan, M. L. Tan, and N. S. Sulaiman, “Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model,” Frontiers in Public Health, vol. 9, Jun. 2021, doi: 10.3389/fpubh.2021.604093.

    [13] Hassani, H., & Mahmoudvand, R. (2013). Multivariate singular spectrum analysis: A general view and new vector forecasting approach. International Journal of Energy and Statistics, 01(01), 55–83.

    [14] Fakhrullah, M., Fuad, M., Shaharudin, S. M., Ismail, S., Ain, N., Samsudin, M., & Zulfikri, M. F. (2021). Comparison of singular spectrum analysis forecasting algorithms for student ’ s academic performance during COVID -19 outbreak. International Journal of Advanced Technology and Engineering Exploration (IJATEE), 8(74), 178–189.

    [15] Elderton, P. (1932). Forecasting mortality. Scandinavian Actuarial Journal, 1932(1–2), 45–64.

    [16] Dong, Y., Zhang, L., Liu, Z., & Wang, J. (2020). Integrated forecasting method for wind energy management: A case study in China. Processes, 8(1).

    [17] Salman, A. O., & Geman, O. (2023). Evaluating Three Machine Learning Classification Methods for Effective COVID-19 Diagnosis. International Journal of Mathematics, Statistics, and Computer Science, 1, 1–14. https://doi.org/10.59543/ijmscs.v1i.7693

    [18] Arif, Z. H., & Cengiz, K. (2023). Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model. International Journal of Mathematics, Statistics, and Computer Science, 1, 25–32. https://doi.org/10.59543/ijmscs.v1i.7715

    Cite This Article As :
    , Kismiantini. , M., Shazlyn. , Setiawan, Adi. , Aditya, Rasyidhani. , Syahida, Salsa-Billa. , Rajoo, Murugan. , Mahdin, Hairulnizam. , A, Salama. Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis. Fusion: Practice and Applications, vol. , no. , 2023, pp. 79-88. DOI: https://doi.org/10.54216/FPA.130106
    , K. M., S. Setiawan, A. Aditya, R. Syahida, S. Rajoo, M. Mahdin, H. A, S. (2023). Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis. Fusion: Practice and Applications, (), 79-88. DOI: https://doi.org/10.54216/FPA.130106
    , Kismiantini. M., Shazlyn. Setiawan, Adi. Aditya, Rasyidhani. Syahida, Salsa-Billa. Rajoo, Murugan. Mahdin, Hairulnizam. A, Salama. Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis. Fusion: Practice and Applications , no. (2023): 79-88. DOI: https://doi.org/10.54216/FPA.130106
    , K. , M., S. , Setiawan, A. , Aditya, R. , Syahida, S. , Rajoo, M. , Mahdin, H. , A, S. (2023) . Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis. Fusion: Practice and Applications , () , 79-88 . DOI: https://doi.org/10.54216/FPA.130106
    K. , M. S. , Setiawan A. , Aditya R. , Syahida S. , Rajoo M. , Mahdin H. , A S. [2023]. Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis. Fusion: Practice and Applications. (): 79-88. DOI: https://doi.org/10.54216/FPA.130106
    , K. M., S. Setiawan, A. Aditya, R. Syahida, S. Rajoo, M. Mahdin, H. A, S. "Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis," Fusion: Practice and Applications, vol. , no. , pp. 79-88, 2023. DOI: https://doi.org/10.54216/FPA.130106

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