In engineering science, the strategies of data-based control synthesis are valuable tools that allow to obtain a control law without the need to identify or to model the system through its related physics. However, the inherent presence of noise when acquiring data represents an obstacle in the validity of these data to represent accurately the system. In this paper, Lyapunov stability theory, dissipativity theory and Petersen's lemma are utilized to develop an algorithm. The proposed algorithm uses sum-of-squares programming to design a control law that guarantees asymptotic stability of polynomial systems with noisy data. In addition, the develop method is tested in comparison to a D-K iteration method in order to evaluate its performance in terms of computational costs.