PREDIKSI KELAYAKAN KREDIT PEMAKAI PONSEL PINTAR DI INDONESIA MENGGUNAKAN ALGORITMA K NEAREST NEIGHBOR (KNN) PASCA PANDEMI
Abstract
Dalam studi ini, kami bertujuan untuk menggunakan algoritma pembelajaran mesin untuk memprediksi kelayakan kredit pemakai ponsel pintar di Indonesia pasca pandemi COVID-19. Algoritma pembelajaran mesin Principal Component Analysis (PCA) dan algoritma K-means digunakan untuk mengurangi ukuran dimensi dataset dan menggolongkan peringkat kepercayaan dari dataset yang berisi 803 responden, termasuk 12 pertanyaan yang disajikan kepada pemakai ponsel pintar Indonesia pasca pandemi COVID-19. Algoritma klasifikasi KNN diterapkan untuk mengklasifikasikan kepercayaan pemakai ponsel pintar di Indonesia. Tes yang dilakukan termasuk akurasi, presisi, recall, dan F1-score. Hasil penelitian ini menunjukkan bahwa algoritma klasifikasi KNN mencapai tingkat akurasi 0,84, tingkat presisi 0,85, tingkat recall 0,84 dan skor F1 0,84.
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