Gender Classification Using Keystroke Dynamics: Enhancing Performance with Feature Selection and Random Forest - Maulina, Ayu; Charisma, Rifqi Alfinnur (Engineering, MAthematics and Computer Science Journal (EMACS), 2025)
The purpose of this study is to improve gender categorization by examining the usage of keyboard dynamics, with enhanced model performance through data standardization and appropriate feature selection. Features including gender, age, handedness, language, education, and metrics measuring typing behavior like mean_latency, std_latency, and frequency are all included in the dataset. Correlation analysis served as the foundation for the feature selection procedure, which is essential for effective model training, and data normalization was performed to guarantee consistency among the characteristics that were chosen. Because of its stability and capacity to handle complicated data, the Random Forest classifier was selected. The findings demonstrate that the Random Forest model achieved an accuracy of 95% and an F1-score of 95% when using all features, and 82% accuracy with an F1-score of 82.5% when using only the selected features. The results emphasize how important it is to choose the appropriate characteristics and standardize the data in order to increase predictive accuracy. By showcasing keystroke dynamics' capacity for gender categorization, this study advances the area and creates opportunities for further research in user experience improvement, digital service customization, and online behavioral analysis. Overall, the study emphasizes the importance of feature engineering, normalization, and model tuning for achieving accurate and reliable classification outcomes.