KLASIFIKASI ANGGOTA KOPERASI AMANAH INSANI MAKMUR MENGGUNAKAN METODE NAIVE BAYES

Cooperatives have long been one of the key pillars supporting the community
economy in Indonesia, with the principles of family values and mutual cooperation as their
foundation. However, in the current digital era, optimizing cooperative data management
has become crucial to ensure effective management. This study aims to optimize credit data
management at the Amanah Insani Makmur (AIM) Cooperative in Bantul, Yogyakarta,
using the Naive Bayes classification method. By classifying credit data, it is hoped that
potential, special, and non-potential customers can be identified more accurately, which
can aid in decision-making related to credit facilities and promotions. The Naive Bayes
method was chosen because it has been proven effective in previous studies, with high
accuracy levels. The data used in this study consists of customer data from AIM
Cooperative, totaling 1004 records, which were divided into 70% training data and 30%
testing data. The results of the study indicate that the Naive Bayes method can provide a
classification accuracy of 86.42%, demonstrating its potential to enhance the efficiency of
credit data management at AIM Cooperative.