Analisis Dampak Karakteristik Siswa pada Masa Pandemi COVID-19 terhadap Prestasi Akademik menggunakan Analisis Diskriminan dan Regresi Multinomial

Cynthia Widodo, Alva Hendi Muhammad, Kusnawi Kusnawi
DOI: https://doi.org/10.33650/jeecom.v6i2.9070



Abstract

Berdasarkan analisis karakteristik siswa di tengah pandemi COVID-19, studi ini menggunakan analisis diskriminan dan regresi multinomial untuk mengeksplorasi dampaknya terhadap prestasi akademik. Faktor-faktor seperti usia, jenis kelamin, tingkat stres, dan transisi ke lingkungan pembelajaran virtual diperiksa untuk memahami pengaruhnya terhadap hasil pendidikan. Temuan ini menyoroti peran penting manajemen stres dan tantangan yang ditimbulkan oleh lingkungan pembelajaran virtual, serta menekankan perlunya intervensi yang ditargetkan untuk mendukung kesejahteraan siswa dan keberhasilan akademik. Analisis diskriminan mengidentifikasi faktor-faktor utama yang membedakan tingkat prestasi akademik, sementara regresi multinomial memodelkan hubungan kompleks di antara variabel-variabel yang mempengaruhi pencapaian siswa. Penelitian ini berkontribusi pada strategi pendidikan yang disesuaikan dengan kebutuhan siswa yang terus berkembang di lanskap pendidikan yang ditransformasi secara digital.

Keywords

Prestasi Akademik; Analisis Diskriminan; Regresi Multinomial; Karakteristik Siswa; Pembelajaran Virtual

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References

B. I. Nugroho, N. A. Santoso, and A. A. Murtopo, “Prediksi Kemampuan Akademik Mahasiswa dengan Metode Support Vector Machine,” remik, vol. 7, no. 1, pp. 177–188, Jan. 2023, doi: 10.33395/remik.v7i1.12010.

M. Jwaifell, “A Proposed Model for Electronic Portfolio to Increase both Validating Skills and Employability,” Procedia - Social and Behavioral Sciences, vol. 103, pp. 356–364, Nov. 2013, doi: 10.1016/j.sbspro.2013.10.345.

E. J. Sintema, “Effect of COVID-19 on the Performance of Grade 12 Students: Implications for STEM Education,” EURASIA J MATH SCI T, vol. 16, no. 7, Apr. 2020, doi: 10.29333/ejmste/7893.

X. Li and H. Wang, “On Mean-Optimal Robust Linear Discriminant Analysis,” in 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA: IEEE, Nov. 2022, pp. 1047–1052. doi: 10.1109/ICDM54844.2022.00129.

K. Kayabol, “Approximate Sparse Multinomial Logistic Regression for Classification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 490–493, Feb. 2020, doi: 10.1109/TPAMI.2019.2904062.

H. Du and H. Li, “Multi-view Canonical Representation Discriminant Analysis,” in 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI), Shanghai, China: IEEE, Jun. 2023, pp. 114–118. doi: 10.1109/CCCAI59026.2023.00029.

J. Kim, Y. Lee, and Z. Liang, “The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1–14, 2022, doi: 10.1109/TPAMI.2022.3192726.

N. Nagananda and A. Savakis, “GILDA++: Grassmann Incremental Linear Discriminant Analysis,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA: IEEE, Jun. 2021, pp. 4448–4456. doi: 10.1109/CVPRW53098.2021.00502.

A. F. Lapanowski and I. Gaynanova, “Compressing large sample data for discriminant analysis,” in 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA: IEEE, Dec. 2021, pp. 1068–1076. doi: 10.1109/BigData52589.2021.9671676.

P. Koochemeshkian and N. Bouguila, “Generalized Dirichlet-Multinomial Regression: Leveraging Arbitrary Features for Topic Modelling,” in 2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Melbourne, Australia: IEEE, Dec. 2023, pp. 884–891. doi: 10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00128.

A. Sofro, K. N. Khikmah, D. Ariyanto, Y. Fuad, B. Rahadjeng, and Y. P. Astuti, “Handling Overdispersion Problems in Multinomial Logistic Regression (Study Case in Stress Level Data),” PROOF, vol. 3, pp. 78–83, Dec. 2023, doi: 10.37394/232020.2023.3.11.

J. Y. V, S. N. S, V. M, and P. Mohideen, “Performance Analysis of a Micromodel-Based Multinomial Classifier,” in 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA: IEEE, Dec. 2022, pp. 275–278. doi: 10.1109/CSCI58124.2022.00052.

K. Wilkinson, K. Seo, R. Pierce, P. Tonellato, J. H. Kim, and D. Myers, “Electronic Medical Record Specialty Group Comparison by Multinomial Logistic Regression,” in 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), Victoria, BC, Canada: IEEE, Aug. 2021, pp. 415–421. doi: 10.1109/ICHI52183.2021.00067.

N. El Allali, M. Fariss, H. Asaidi, and M. Bellouki, “Multinomial Naive Bayes Categorization for Semantic Web Services,” in 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), Marrakech, Morocco: IEEE, Jun. 2021, pp. 74–79. doi: 10.1109/ICDATA52997.2021.00023.


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Journal of Electrical Engineering and Computer (JEECOM)
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