The Indonesian sign language, BISINDO, is extensively adopted to facilitate communication between hearing and deaf individuals in Indonesia. However, existing deep learning models, such as ResNet-50, are computationally intensive and require significant resources for deployment, making them less practical for real-world applications in resource-constrained environments. This research addresses these challenges by employing magnitude-based structured pruning techniques with a polynomial decay schedule to optimize the ResNet-50 architecture for BISINDO alphabet recognition. The objective is to create an efficient and accurate recognition system tailored for devices with limited resources. The study evaluates the trade-offs between model accuracy and size reduction across sparsity levels ranging from 40% to 80%. The findings indicate that the baseline model attains an accuracy rate of 96.27%, with a file size of 94.88 MB. In contrast, the pruned version, exhibiting 60% sparsity, achieves a slightly lower accuracy of 95.73%, alongside a substantial file size reduction of 52.28%, resulting in a new size of 45.28 MB. Moreover, active parameters decreased by 59.63%, highlighting structured pruning's ability to optimize both performance and efficiency, thereby increasing the applicability of deep learning models for BISINDO recognition in practical settings.