Implementation of Decision Tree Method to Predict Customer Interest in Internet Data Packages
Abstract
The use of smartphones is on the rise, making internet packages vital in everyday life. In Indonesia, mobile operators offer a variety of data packages to meet customer needs. Understanding preferences is important to making the right decisions when providing products that meet customer needs. However, not all packages are suitable for all customers. Telkomsel is one of the providers that can deliver consistent signals and a wide range of data packages, but it still has a relatively high price. The study uses decision tree methodology to analyze citizens' preferences about Telkomsel services, comparing the relative cost of data packages with other services. This research will use surveys with samples from different communities to determine the representativeness of the results and provide strategic recommendations for Telkomsel to improve customer satisfaction. The research methods employed in this study included data collection, preprocessing, data division, model design, prediction results, and result evaluation. The results showed accuracy levels of 98.7%, precision of 100%, recall of 98.7%, specificity of 100%, and an F1-score of 99.3%. This study demonstrates the effectiveness of the decision tree model in predicting customer interest in Telkomsel services. Despite some limitations, the findings provide valuable insights that Telkomsel can use to develop more effective marketing strategies.
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