CHIN SIANG LIMESRAA FAISAL MALIKKHAI WAH KHAWALHAMZAH ALNOORXINYING CHEWZHI LIN CHONGMariam Al Akasheh2024-10-152024-10-152023-10-27https://doi.org/10.19139/soic-2310-5070-1799https://dspace-cris.utar.edu.my/handle/123456789/2019<jats:p>Organizations strive to retain their top talent and maintain workforce stability by predicting employee turnover and implementing preventive measures. Employee turnover prediction is a critical task, and accurate prediction models can help organizations take proactive measures to retain employees and reduce turnover rates. Therefore, in this study, we propose a hybrid genetic algorithm–autoencoder–k-nearest neighbor (GA–DeepAutoencoder–KNN) model to predict employee turnover. The proposed model combines a genetic algorithm, an autoencoder, and the KNN model to enhance prediction accuracy. The proposed model was evaluated and compared experimentally with the conventional DeepAutoencoder–KNN and k-nearest neighbor models. The results demonstrate that the GA–DeepAutoencoder–KNN model achieved a significantly higher accuracy score (90.95\%) compared to the conventional models (86.48% and 88.37% accuracy, respectively).  Our findings are expected to assist HR teams identify at-risk employees and implement targeted retention strategies to improve the retention rate of valuable employees. The proposed model can be applied to various industries and organizations, making it a valuable tool for HR professionals to improve workforce stability and productivity.</jats:p>journal-article