IMPLEMENTASI ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN K-NEAREST NEIGHBOR (KNN) UNTUK MEMPREDIKSI KELAYAKAN KREDIT PENGGUNA SMARTPHONE DI INDONESIA PADA MASA PANDEMI COVID-19

  • Rara Kartika Kusuma Winahyu Politeknik Astra, Jawa Barat
  • Vian Ardiyansyah Saputro Politeknik Astra, Jawa Barat
DOI: http://dx.doi.org/10.46930/ojsuda.v31i3.3207

Abstract

Dalam penelitian ini, kami bertujuan untuk memprediksi kelayakan kredit pengguna smartphone di Indonesia selama masa Pandemi COVID-19 menggunakan algoritma  machine learning. Algoritma Principal Component Analysis (PCA) dan K-means digunakan untuk mereduksi dimensi dataset dan mengelompokkan kelayakan kredit  dari sejumlah 1050  data set  responden yang terdiri dari dua belas pertanyaan kepada pengguna smartphone di Indonesia selama pandemi COVID-19. Algoritma klasifikasi K-Nearest Neighbor (KNN) juga digunakan untuk mengklasifikasikan kelayakan kredit pengguna smartphone di Indonesia. Pengujian yang dilakukan meliputi pengujian akurasi, presisi, recall, dan F1-score. Hasil dari penelitian ini menunjukkan bahwa algoritma klasifikasi K-Nearest Neighbor memiliki akurasi sebesar 0.85, presisi sebesar 0.87, recall 0.84, dan F1 score sebesar 0.85 dalam mengklasifikasikan kelayakan kredit pengguna smartphone di Indonesia pada masa pandemi COVID-19.

Published
Jun 7, 2023
How to Cite
WINAHYU, Rara Kartika Kusuma; SAPUTRO, Vian Ardiyansyah. IMPLEMENTASI ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN K-NEAREST NEIGHBOR (KNN) UNTUK MEMPREDIKSI KELAYAKAN KREDIT PENGGUNA SMARTPHONE DI INDONESIA PADA MASA PANDEMI COVID-19. Jurnal Darma Agung, [S.l.], v. 31, n. 3, p. 1-10, june 2023. ISSN 2654-3915. Available at: <https://jurnal.darmaagung.ac.id/index.php/jurnaluda/article/view/3207>. Date accessed: 22 dec. 2024. doi: http://dx.doi.org/10.46930/ojsuda.v31i3.3207.
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Vol 31 No 3 (2023): JUNI
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