Penerapan Algoritma Naïve Bayes Classifier dan Decision Tree untuk Memprediksi Tingkat Kepuasan Pelanggan Teras Coffe Rantauprapat

Abstract

The increasingly fierce competition in the coffee shop business requires business owners to understand customer satisfaction levels as a basis for improving service quality and maintaining customer loyalty. Therefore, an approach capable of accurately identifying and predicting customer satisfaction levels based on customer data is needed. Data mining is a data processing technique that can be used to discover patterns and important information from data sets to support the decision-making process. In this study, the Naïve Bayes Classifier and Decision Tree algorithms were used because both are classification methods capable of generating predictions based on the characteristics of the data. The research method used was a quantitative method by utilizing Teras Coffee Rantauprapat customer questionnaire data which was then processed using the Orange Data Mining application. The research data was divided into training data and testing data to build and test the classification models generated by both algorithms. The results showed that the Naïve Bayes and Decision Tree algorithms were able to classify customer satisfaction levels into satisfied and dissatisfied categories with a good level of accuracy. Based on the model evaluation results, the Naïve Bayes algorithm obtained superior performance compared to Decision Tree based on higher AUC, Precision, F1-Score, and MCC values. Thus, both algorithms can be applied to predict customer satisfaction levels, but Naïve Bayes proved more optimal in generating predictions on the dataset used in this study. The results of this study are expected to serve as a reference for Teras Coffee Rantauprapat in continuously improving service quality and customer satisfaction.