Stance detection in NLP involves determining whether an author is supportive, against, or neutral towards a particular target. This task is particularly challenging for Turkish due to the limited availability of data, which hinders progress in the field. To address this issue, we introduce a novel dataset focused on stance detection in Turkish, specifically within the political domain. This dataset was collected from X (formerly Twitter) and annotated by three human annotators who followed predefined guidelines to ensure consistent labeling and generalizability. After compiling the dataset, we trained various transformer-based models with different architectures, showing that the dataset is effective for stance classification. These models achieved an impressive Macro F1 score of up to 82%, highlighting their effectiveness in stance detection.