<![CDATA[In the rapidly evolving digital era, finding relevant journals has become a challengefor students, researchers, and academics. The easy access to journal publications hasled to an increase in the number of available journals. Therefore, there is a need fora recommendation system based on related journals automatically using the TF-IDFand K-Nearest Neighbor (KNN) approaches with cosine distance. The goal is toenhance the efficiency and accuracy of journal searches. The first step is to process the text taken from the titles and abstracts of the journals into numerical vectors using TF-IDF to determine the importance of words in each document. Then, KNN is used to find the journals that are most similar to the specified journal based on the distance between TF-IDF vectors. Precision@3 is used to evaluate the results of the top three recommendations. The evaluation results show highly relevant recommendations, with a Precision@3 value of 1. This system has successfully improved the efficiency and accuracy of automatic journal searches for relevant content]]>
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