<![CDATA[The availability of a workforce that aligns with company requirements significantly affects organizational productivity and success. The dissemination of job vacancy information, which is still carried out manually through bulletin boards, newspapers, and job fairs, has limitations in terms of reach and efficiency. On the other hand, job seekers often face difficulties in finding employment opportunities that match their qualifications and expertise. This study aims to design a job and workforce recommendation application based on text similarity using the TF-IDF and Cosine Similarity methods. The research process begins with text preprocessing, including case folding, tokenizing, filtering, and stemming applied to job vacancy and workforce profile data. Furthermore, word weighting is performed using the TF-IDF method, followed by similarity measurement using Cosine Similarity. The testing results on three workforce profiles indicate that the system is capable of ranking candidates based on their similarity level to job vacancies, with the highest similarity score reaching 1.03%, followed by 0.67% and 0.20%. Although the similarity values are relatively low due to the mismatch between workforce backgrounds and job requirements, the system is able to provide objective recommendations based on data relevance.]]>
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