Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier

Abstract

This study aims to predict the smoothness of installment payments in cooperatives, making it easier for staff to analyze credit lending. Lack of prudence in analyzing credit results in customers who are in arrears in paying installments, resulting in bad credit. To minimize errors that exist, it is necessary to evaluate the provision of loans to prospective debtors. By utilizing past member criteria data in the past that will be used to predict smooth payments using data mining. The data mining technique used is the Naive Bayes classifier method. The prediction process uses the naive Bayes method, namely by determining the probability or opportunity based on the previous member's data, and the results are used to help make a decision. The criteria used are member data: employment, income, house status, number of credits, and type of credit. Based on the naive Bayes method, the results obtained are 90.00% accuracy, 0.880% AUC, 83,33% recall, and 100% precision.

Klasifikasi Dana Hibah Usaha Mikro Kecil dan Menengah dengan Metode Naïve Bayes

“…Tahun 2020 Rizal Amegia Saputra [12] melakukan penelitian tentang Pemerolehan Informasi Pada Algoritma C4.5 Untuk Klasifikasi Penerimaan Bantuan Pangan Non Tunai (BPNT). Peneltian tersebut mengunakan 130 dataset yang akan di klasifikasikan berdasarkan kriteria KEMENSOS.…”

“…maka dari itu nilai independensi yang ada dari beberapa variabel akan di hitung akan dapat mempengaruhi dalam menentukan kelas yang akan di tentukan. Rumusan Masalah pada metode ini ialah [12]:…”