<![CDATA[Education plays a critical role in shaping career decisions for the future. However, many students encounter difficulties in selecting suitable academic programs, often stemming from a lack of confidence in their ability to make appropriate decisions. Consequently, students may choose study programs that do not align with their personal characteristics. This study emphasizes the importance of providing comprehensive information about various academic programs offered in higher education and developing tools to assist prospective students in making informed decisions. To address these challenges, a recommendation system using Hybrid Filtering technology has been developed. The system integrates Content-Based Filtering and Collaborative Filtering methods within the TensorFlow Recommenders System (TFRS) framework. The study utilized data from undergraduate students of the Faculty of Mathematics and Natural Sciences (FMIPA) across seven academic programs. By employing 10 features representing students' interests and talents, the recommendation system generated accurate and tailored suggestions for study programs. The model was trained and evaluated using both real and augmented (augmented) datasets with predefined hyperparameters. Results demonstrated that using only the real dataset achieved a Top-1 accuracy of 0.59 and a Top-5 accuracy of 0.97. When incorporating the augmented dataset, the Top-1 accuracy improved to 0.66, while the Top-5 accuracy reached 1.0. The findings reveal that combining real and augmented datasets enhances average accuracy by approximately 10% compared to using the real dataset alone. Additionally, the study program recommendations produced by the model showed significant improvement in quality. A web-based recommendation system utilizing the TFRS model was developed and positively evaluated by FMIPA students. User feedback indicated high satisfaction with the system's recommendations, demonstrating its effectiveness in guiding students toward suitable academic programs.]]>
