PREDIKSI PENDAFTARAN PESERTA DIDIK BARU DENGAN METODE POLYNOMIAL REGRESSION, DAN K-MEDOIDS
indonesia
Keywords:
polynomial regression, K-Medoids, Prediksi pendaftaran
Abstract
One school attempted to predict the acceptance of new students based on data from the previous year, but the results were inaccurate. The fluctuation in the number of new student admissions is a problem for SMK Negeri 2 Kotabumi in preparing class facilities, uniforms, books to support learning activities and determining steps and policies related to school promotion and targets for new student admissions in the following years. Predicting new student enrollment using the polynomial regression method, and K-Medoids, in processing student enrollment prediction data. The results obtained are Y values that are in accordance with the implementation results using python. For example, in 2018 the value Y = 0.0034x6 - 0.6194x5 + 46.754x4 - 1864.6x3 + 41412x2 - 485358x + 2E+06 = 1744.01 with R = 0.8779 accompanied by the same for each year, whereas for the K- Medoids method obtained in 2018 clustering 0 obtained 73 prospective students in the non-passing category and 19 in the pass category, while for 2019 to 2022 the number of cluster 0 is worth 0 and cluster 1 is worth 92 which means that all participants have passed
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[3] H. Thamrin, “Analyzing and Forecasting Admission data using Time Series Model,” Jurnal Online Informatika, vol. 5, no. 1, pp. 35–44, 2020, doi: 10.15575/join.v5i1.546.
[4] A. Afandi, N. Noviana, and D. Nurdianah, “Naive Bayes Method and C4.5 in Classification of Birth Data,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 4, p. 435, Oct. 2022, doi: 10.22146/ijccs.78198.
[5] E. Alothali, H. Alashwal, and S. Harous, “Data stream mining techniques: A review,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 728–737, Apr. 2019, doi: 10.12928/TELKOMNIKA.v17i2.11752.
[6] D. A. Ferryan, P. K. Intan, and M. Hafiyusholeh, “Peramalan Harga Minyak Mentah di Indonesia dengan Metode Regresi Polinomial,” JURNAL ILMIAH MATEMATIKA DAN TERAPAN, vol. 19, no. 1, pp. 13–18, Jun. 2022, doi: 10.22487/2540766x.2022.v19.i1.15779.
[7] P. Kebakaran Hutan, L. Berdasarkan Persebaran Titik Panas Dyang Falila Pramesti, M. Tanzil Furqon, and
C. Dewi, “Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data,” 2017. [Online]. Available: http://j-ptiik.ub.ac.id
[8] E. Vivian Astuti, A. Afandi, and D. Marisa Efendi, “Classification and Clustering of Internet Quota Sales Data Using C4.5 Algorithm and K-Means ARTICLE INFORMATION ABSTRACT,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 2, pp. 268–283, 2023, doi: 10.26555/jiteki.v9i2.25970.
[9] K. Natarajan, J. Li, and A. Koronios, “Data mining techniques for data cleaning,” Engineering Asset Lifecycle Management - Proceedings of the 4th World Congress on Engineering Asset Management, WCEAM 2009, no. September, pp. 796–804, 2009, doi: 10.1007/978-0-85729-320-6_91.
[10] Xiaoling Shu, Knowledge Discovery in the Social Sciences: A Data Mining Approach. okland, california: University of California Press, 2020.
[11] R. Muliono and Z. Sembiring, “DATA MINING CLUSTERING MENGGUNAKAN ALGORITMA K-MEANS UNTUK KLASTERISASI TINGKAT,” vol. 4, no. 2, pp. 272–279, 2019.
[12] R. Hasan, S. Palaniappan, S. Mahmood, K. U. Sarker, and A. Abbas, “Modelling and predicting student’s academic performance using classification data mining techniques,” Int J Bus Inf Syst, vol. 34, no. 3, pp. 403– 422, 2020, doi: 10.1504/IJBIS.2020.108649.
[13] T. R. Stella Mary and S. Sebastian, “Predicting heart ailment in patients with varying number of features using data mining techniques,” International Journal of Electrical and Computer Engineering, vol. 9, no. 4, pp. 2675–2681, 2019, doi:10.11591/ijece.v9i4.pp2675-2681.
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[18] J. R. Edwards, “Polynomial regression and response surface methodology 1 Polynomial Regression and Response Surface Methodology.”
Published
2023-10-16
How to Cite
[1]
N. ., J. Fartesa, C. Fauzi, and S. ., “PREDIKSI PENDAFTARAN PESERTA DIDIK BARU DENGAN METODE POLYNOMIAL REGRESSION, DAN K-MEDOIDS”, Jurnal Informasi dan Komputer, vol. 11, no. 02, pp. 242-247, Oct. 2023.
