<![CDATA[The selection of a final project topic is a crucial stage in the academic journey of students, as it determines the direction of research while serving as a means to apply the knowledge acquired during their studies. However, in practice, many students experience difficulties in choosing a topic that aligns with their interests and areas of expertise. This challenge is largely attributed to the absence of systems capable of providing personalized recommendations. To address this issue, this study develops a final project topic recommendation system by integrating the Latent Dirichlet Allocation (LDA) algorithm with a cookie-based approach to accommodate user preferences. The dataset used consists of 200 final project documents from the Informatics Engineering program, with titles and abstracts serving as the primary features for topic modeling during model training and perplexity evaluation. In addition, users’ search histories and relevance feedback stored in cookie sessions are utilized as personalization features to generate more tailored recommendations. FastText is employed to produce more contextual word vector representations, while cosine similarity is applied to measure the closeness between search keywords and final project topic documents. Evaluation results based on perplexity indicate that the model with 22 topics yields the most statistically optimal performance. Furthermore, testing using Click-Through Rate (CTR) demonstrates that the combination of topic modeling and user preference personalization produces the highest relevance, achieving a CTR of 15.6%, which is significantly higher than the baseline CTR of 2.2%. These findings confirm that the proposed system is capable of delivering more relevant, adaptive, and user-oriented final project topic recommendations.]]>
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