Pengaruh Seleksi Fitur Information Gain pada K-Nearest Neighbor untuk Klasifikasi Tingkat Kelancaran Pembayaran Kredit Kendaraan - Ulfah Mutmainnah; Budi Darma Setiawan; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Intermittent credit is one of the problems or risks that are often faced by some auto loan service providers. The problem stems from the debtor's behavior, namely not paying the installments on time. In determining the smoothness of credit payments depends on the analysis of debtor data, but analyzing for large amounts of data can take up more time. This study uses the Information Gain feature selection and the K-Nearest Neighbor algorithm to overcome the problem of effectiveness and determine the accuracy of the classification level of the smoothness of auto loan payments so as to determine the effect of feature selection. Information Gain feature selection which is used to reduce feature dimensions so that relevant features can be obtained. The selected features are then processed for classification using the K-Nearest Neighbor algorithm. Based on testing from this study, the highest accuracy obtained is 94.44% when testing with a balanced class distribution using the number of features 3 and the value of K = 4 while the lowest accuracy is obtained at 33.33% using the number of features 10 with a value of K = 5 when testing with uneven class distribution. Features that produce the highest accuracy are jobs, income and price on the road (OTR). The three features are features with the largest order of gain values and have a gain value of more than 0.1.