Abstract
This paper presents the effectiveness of adopting survival analysis approach to predict the WiFi usage in the future and the understanding of covariance affect WiFi usage such as date, time, and user, by introducing dataset of WiFi usage historical. The study took place in National Formosa University in Taiwan. Survival analysis is the analysis of data involving times to event of interest. There are three survival analysis methods implemented in this paper which are Kaplan-Meier estimator, Cox Proportional Hazards Model, and Random Survival Forest. The result was shown that survival analysis approach gains a satisfy prediction result. This approach can be adapted for improving WiFi network organization in any organization by understanding the connection of covariance and accomplishing an effective decision.
Published Version
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