<![CDATA[Handwriting reflects a person's unique traits and has long been studied in the field of graphology to uncover personality characteristics. However, traditional graphological analysis is subjective, time-consuming, and prone to inter-rater differences. This study aims to develop a PenaKepribadian mobile application using ResNet50 transfer learning to automatically identify Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). The HiEnWrite dataset contains 327 English handwritten images annotated by a certified graphologists, was utilized with an 80:20 train-test split. Three optimizers such as SGD, RMSprop, and Adam were comparatively evaluated. Adam achieved the best performance with a training PCC of 0.5872 with 91.90% accuracy and a testing PCC of 0.4719 with 89.95% accuracy, outperforming both SGD and RMSprop. A testing PCC of 0.4719 indicates moderate correlation, suggesting promising yet improvable results. Robustness testing across varying lighting conditions, paper backgrounds, and writing media showed consistent performance, with mean prediction deviations ranging from 0.070 to 0.126. All Black Box Testing scenarios returned valid results. These findings confirm that ResNet50 transfer learning effectively extracts handwriting features for personality prediction, though further improvements remain necessary before high-stakes deployment. This research contributes to personality computing and opens avenues for efficient, automated, and accessible personality assessment systems.]]>
