Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes

Abstract

Customers are people who trust the management of their money in a bank or other financial service party to be used in banking business operations, thereby expecting a return in the form of money for their savings. To reach information to increase company profits, a method is needed to be able to provide knowledge in supporting the data that the company has. The model can be obtained by using predictive data processing of customer data that is categorized as potential or not potential. Data processing can be done using Machine Learning, namely classification techniques. This technique will produce a churn prediction model for determining the category of customers who fall into the Potential or Not Potential category and find out what accuracy value will be generated by applying the classification technique using the Naïve Bayes Algorithm. The parameters used in this study are Gender, Age, Marital Status, Dependent, Occupation, Region, Information. The data used are 150 data from customers who have participated in the savings program to find out whether the customer is in the Potential or Non-Potential category. The accuracy results generated using this data are 86.17% of the tools used by Rapidminner.

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“…One topic of machine learning research that is often carried out is regression or prediction. Several studies regarding the implementation of machine learning for prediction are research conducted by (Fitrianah et al 2021) which predicts house prices using linear regression. Then paper by (Fitrianah et al 2021) researched expect potential customers using the naïve Bayes algorithm.…”

“…Several studies regarding the implementation of machine learning for prediction are research conducted by (Fitrianah et al 2021) which predicts house prices using linear regression. Then paper by (Fitrianah et al 2021) researched expect potential customers using the naïve Bayes algorithm. The previous research from (Edric and Tamba 2022) used a random forest algorithm to predict heart failure.…”