<![CDATA[The telecommunications sector is facing increasing competition, and customer churn is still a majorchallenge despite the implementation of advanced promotions and high-quality services. Churn refers tothe discontinuation of services by customers, influenced by several factors that can be found through datamodeling. This study compares two predictive models, Random Survival Forest (RSF) and Fuzzy RandomSurvival Forest (FRSF), for predicting customer churn time in the telecommunications industry. Bothmodels are evaluated using the median C-index value obtained from 20 repetitions, ensuring moreconsistent and reliable results. RSF, a widely used survival analysis method, has shown strong predictivepower, with studies reporting up to 99% accuracy in churn prediction. However, FRSF, a modified versionthat incorporates fuzzy logic, has proved superior performance, particularly in handling imprecise oruncertain data. The results show that FRSF achieves a lower error rate of 0.1739, compared to RSF's errorrate of 0.1906. These findings suggest that FRSF outperforms RSF in churn prediction, making it a morereliable and righter model for finding at-risk customers. The study concludes that the FRSF model is thepreferred choice for predicting churn in the telecommunications industry, offering better predictive qualityand consistency in handling uncertain data.]]>
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