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