<![CDATA[The selection of an appropriate encryption algorithm is crucial in ensuring data security, as each algorithm has distinct advantages and disadvantages in terms of speed, efficiency, and security level. Many users struggle to determine the most suitable algorithm due to limited technical knowledge and the vast amount of literature that must be reviewed. Therefore, this study proposes a recommendation system based on Content-Based Filtering (CBF) integrated with text mining to facilitate faster, more accurate, and data-driven algorithm selection. The objective of this research is to develop a recommendation system capable of analyzing the technical characteristics of encryption algorithms from scientific literature and providing relevant suggestions according to user needs. The methodology includes collecting 300 articles from the Garuda Kemdikbud portal using web scraping, performing data preprocessing such as tokenization, stop word removal, and case folding, representing text with TF-IDF, and calculating similarity using Cosine Similarity. The results indicate that the most frequently discussed algorithms are RSA (52 articles), AES (40 articles), and RC4 (25 articles), reflecting research trends focusing on modern public-key and symmetric cryptography. The evaluation results show that the system achieved Precision@3 of 1.0000 and Average Precision (AP) of 0.0583, indicating that the top recommendations generated are highly relevant to user needs. The developed system successfully generated recommendations tailored to specific needs, such as suggesting AES as the primary choice for “fast encryption of sensitive data.” This study demonstrates that combining text mining and CBF is effective in assisting the selection of encryption algorithms through literature-based analysis.]]>
