<![CDATA[Selecting a thesis topic is a critical stage in a student’s academic journey and frequently poses substantial cognitive and procedural challenges. This study reports the design and implementation of the Computer Science Thesis Recommendation System (SRSIK Hub), a web-based decision-support platform aimed at improving the efficiency and accuracy of thesis topic selection. The primary novelty of this research lies in the systematic integration of Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity within a large-scale academic corpus to model fine-grained semantic relevance between student interests and prior thesis documents, enabling more precise and transparent recommendations than conventional keyword-based searches. The system adopts a content-based filtering approach and processes approximately 4,000 thesis records collected from multiple university repositories. Textual data are preprocessed and transformed using TF-IDF vectorization, while Cosine Similarity is employed to rank candidate topics according to relevance. System effectiveness was evaluated using the WebUse Framework involving 75 student respondents. The evaluation yielded an overall score of 4.44 out of 5, indicating high usability, strong information quality, and reliable system functionality. This performance score demonstrates that the proposed recommendation model is not only technically sound but also practically applicable in real academic settings, where it can significantly reduce topic selection time and uncertainty for students. The results confirm that SRSIK Hub effectively supports students in identifying research topics aligned with their academic interests and competencies. Beyond local deployment, the system is transferable to other institutions for scalable thesis recommendation support.]]>
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