Application of Data Mining Classification to Store Customer Satisfaction Bombay Textiles - Siti Sundari; Agus Perdana Windarto; Yuegilion Pranayama Purba (JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2022)
This study aims to obtain a model of rules in classifying the level of customer satisfaction at Bombay Textile Stores. By knowing the level of customer satisfaction, shop owners can improve service if it is not good and further improve service if the level of satisfaction is good. This study measures the level of customer satisfaction at the Bombay Textile Store. The method used in this study is the C4.5 Algorithm, where the data source used is a questionnaire/questionnaire technique given to Bombay Textile Store customers. The variables used include Service, Quality of Goods, Price, Facilities, and Promotion. The results obtained 11 rules for the classification of customer satisfaction levels with 5 rules satisfied status and 6 rules dissatisfied status. The C4.5 algorithm can be used in the case of customer satisfaction levels with an accuracy rate of 96.67%. From the results of the analysis, it is hoped that it can be applied so that it can be used as a decision to improve service to customers.