Smart Journal Finder: A Web-Based Scientific Article Categorization Using Jaccard Similarity


Rodiah Rodiah(1*);

(1) Universitas Gunadarma
(*) Corresponding Author

  

Abstract


The rapid growth of scientific publications presents challenges for researchers in identifying appropriate journals for manuscript submission. With an overwhelming number of journals across diverse disciplines, manually matching a manuscript to a suitable journal becomes inefficient and prone to misclassification. This study proposes the Smart Journal Finder, a web-based system designed to recommend relevant scientific journals by analyzing textual similarities between user-submitted manuscripts and indexed journal articles. The system processes input data including the title, abstract, keywords, and field of study through several stages: preprocessing, stop word removal, stemming using the Nazief-Adriani algorithm, and duplicate term elimination. Similarity scoring is performed using the Jaccard Similarity algorithm, followed by ranking the results and displaying journal metadata such as subject, publisher, and citation metrics. Results show that the system accurately transforms and filters input text, effectively calculates similarity scores, and successfully matches manuscripts to appropriate journals. By automating this process, the Smart Journal Finder enhances the efficiency of journal selection, improves the relevance of publication targets, and supports researchers in increasing the visibility and impact of their work. However, the current implementation is limited to Indonesian-language journals and does not yet incorporate semantic similarity or multilingual processing. Future work will focus on expanding coverage across disciplines and integrating more advanced similarity models.


Keywords


Document Classification; Jaccard Similarity; Journal; Recommendation System; Publication;

  
  

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doi  https://doi.org/10.33096/ilkom.v18i1.2814.17-29 		  

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