Markov state models (MSMs) are a powerful approach for analyzing the long-time behaviors of protein motion using molecular dynamics simulation data. However, their quantitative performance with respect to the physical quantities is poor. We believe that this poor performance is caused by the failure to appropriately classify protein conformations into states when constructing MSMs. Herein, we show that the quantitative performance of an order parameter is improved when a manifold-learning technique is employed for the classification in the MSM. The MSM construction using the K-center method, which has been previously used for classification, has a poor quantitative performance.