We have developed composite measures of supercomputer technology, reflecting various factors of supercomputers using Martino's scoring model. CPUs, accelerators, memory, interconnection networks, and power consumption are chosen as factors of the model. The weight values of the factors are derived based on a survey of 129 domestic and international experts. The measured values are then standardized to integrate measurement units of the factors in the model. This model has been applied to 50 supercomputers, and rank correlation analysis was performed using representative measures. As a consequence, the ranking drastically changes except for the 1st and 2nd supercomputers on the TOP500. In addition, the characteristics of memory and interconnection networks influence the ranking, and the results demonstrate that the proposed model has low correlations with HPL and HPCG but a high correlation with Green500. This indicates that power consumption is an important factor that has a significant effect on the measures of supercomputer technology. In addition, it is determined that the differences between the HPL ranking and the proposed model ranking are influenced by power consumption, CPU theoretical peak performance, and main memory bandwidth in order of significance. In conclusion, the composite measures proposed in this study are more suitable for comprehensively describing supercomputer technology than existing performance measures. The findings of this study are expected to support decision making related to management and policy in the procurement and operation of supercomputers.