We study community detection in the \emph{symmetric $k$-stochastic block model}, where $n$ nodes are evenly partitioned into $k$ clusters with intra- and inter-cluster connection probabilities $p$ and $q$, respectively. Our main result is a polynomial-time algorithm that achieves the optimal misclassification rate $\exp(-(1 \pm o(1)) C/k)$, where $C = (\sqrt{pn} - \sqrt{qn})^2$, whenever $C \geq K k^2 \log k$ for some universal constant $K$, matching the Kesten–Stigum ({KS}) threshold up to a $\log k$ factor. Notably, this rate holds even when an adversary corrupts an $\eta \leq \exp(-(1 \pm o(1)) C/k)$ fraction of the nodes. To the best of our knowledge, this optimal error rate was previously only attainable either via computationally inefficient procedures (Zhang and Zhou, 2015) or via polynomial-time algorithms that require strictly stronger assumptions such as $C \geq K k^3$ (Gao et al., 2017). In the node-robust setting, the best known algorithm requires the substantially stronger condition $C \geq K k^{102}$ (Liu and Moitra, 2022). Our results close this gap by providing the first polynomial-time algorithm that achieves the optimal error rate near the {KS} threshold in both settings. Our work has two key technical contributions: (1) we robustify majority voting via the Sum-of-Squares framework, (2) we develop a novel graph bisectioning algorithm via robust majority voting, which allows us to significantly improve the misclassification rate to $1/\mathrm{poly}(k)$ for the initial estimation near the {KS} threshold.