Prediksi nilai akademik peserta didik di masa pandemi covid-19 dengan regresi linier berganda
Abstract
With the implementation of Learning From Home (BDR) learning patterns, there are several problems that exist, namely the decline in student achievement in learning outcomes, decreased interest in learning and the presence of students in online learning activities, and decreased participation of parents/guardians of students in paying attention to learning conditions and achievements of students at SDN Cengkareng Timur 01 Pagi. On the basis of all that research related to predicting the academic value of students will later be able to help the school to make permanent decisions on the problems that occur in the school environment. This study, using the multiple linear regression method, is effectively used to predict the final grades of students, so that by predicting school institutions can take preventive steps to boost student grades. The test results from 3 tools, namely Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE) show that the prediction results are accurate enough to be used as a predictive model. Therefore, it is hoped that the prediction model will periodically test the prediction model every semester and applications that can be used directly without having to use a spreadsheet.
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References
[2] Kementerian Pendidikan Dan Kebudayaan, “Pedoman Penyelenggaraan Belajar Dari Rumah Dalam Masa Darurat Penyebaran Corona Virus Disease (Covid-19),” Surat Edaran Nomor 15 Tahun 2O2O, no. 021, pp. 1–20, 2020.
[3] I. G. Jakarta, “16 Tahun 2020,” 2020.
[4] Dinas Pendidikan Provinsi DKI Jakarta, “Surat Edaran Nomor 27/Se/2020 Tentang Pembelajaran Di Rumah (Home Learning),” pp. 5–6, 2020.
[5] A. Amalia and N. Sa’adah, “Dampak Wabah Covid-19 Terhadap Kegiatan Belajar Mengajar Di Indonesia,” J. Psikol., vol. 13, no. 2, pp. 214–225, 2020, doi: 10.35760/psi.2020.v13i2.3572.
[6] M. Siahaan, “Dampak Pandemi Covid-19 Terhadap Dunia Pendidikan,” J. Kaji. Ilm., vol. 1, no. 1, pp. 73–80, 2020, doi: 10.31599/jki.v1i1.265.
[7] G. Farell, H. K. Saputra, and I. Novid, “Rancang Bangun Sistem Informasi Pengarsipan Surat Menyurat (Studi Kasus Fakultas Teknik Unp), ” J. Teknol. Inf. dan Pendidik., vol. 11, no. 2, pp. 56–62, 2018.
[8] P. E. S. dan L. S. Sudjiman, “KOMPUTER DALAM PROSES PENGAMBILAN KEPUTUSAN Paul Eduard Sudjiman dan Lorina Siregar Sudjiman COMPUTER BASED MANAGEMENT INFORMATION SYSTEM,” J. TeIKa, vol. 8, pp. 55–67, 2018, [Online]. Available: https://jurnal.unai.edu/index.php/teika/article/view/2327
[9] L. Ploywattanawong, “Decision Support System Model,” pp. 171–175, 2017, doi: 10.1145/3029387.3029402.
[10] Abdul Khadir, “Sistem Pendukung Keputusan,” Sist. Pendukung Keputusan, vol. 8, pp. 1–7, 2014.
[11] A. Yulianto, “Prediksi Pertumbuhan Jumlah Unit Usaha Mikro Kecil dan Menengah ( UMKM ) Menggunakan Metode Regresi Linier Berganda,” pp. 1–12, 2021.
[12] E. E. Riyana, “Pengaruh Pembiasaan Baru Pandemi Covid-19 dan Model Pembelajaran Blended Learning PAI dan Budi Pekerti Terhadap Minat Belajar Siswa di SMPN 1 Siman …,” 2021, [Online]. Available: http://etheses.iainponorogo.ac.id/id/eprint/15424
[13] W. Riswandi, D. Irwan, E. Sulastri, and D. Gustian, “Pengaruh Belajar dari Rumah (BDR) tehadap Prestasi Siswa dengan Regresi Linier Berganda di Masa Pandemi Covid-19,” Semin. Nas. Inform. 2020 (SEMNASIF 2020), vol. 1, no. 1, pp. 187–195, 2020.
[14] D. T. Larose, Data Mining Methods and Models. Hoboken, New Jersey: Johns Wiley& Sons, 2006. doi: 10.1002/0471756482.
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