Prediksi Kelancaran Piutang Pelanggan pada PT. Citra Ina Feedmill dengan Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbors

Giving credit to customers is a solution that is often done by business actors today, such as companies. By offering credit solutions, companies can attract potential customers and make it easier for customers to make payments. However, being able to provide credit to customers can cause losses for the company when customers are unable to pay their periodic credits. For this reason, in this study an analysis was carried out to categorize customer payment capabilities using the Naïve Bayes Algorithm and the K-Nearest Neighbors Algorithm. The results of the study, obtained the best data mining model to predict the age classification of customer receivables using the KNN algorithm which is optimized with a feature selection algorithm with an accuracy of 99%. Keywords: algorithm, data mining, KNN, Naïve Bayes, customer receivable