<![CDATA[The rapid expansion of digital recruitment platforms has intensified information overload, making it increasingly difficult for job seekers to identify vacancies aligned with their skills and professional interests. In response to this challenge, this study develops a semantic-based job recommendation system that leverages word embedding and cosine similarity to enhance retrieval relevance within the Indonesian labor market context. The primary contribution lies in the empirical examination of embedding-driven semantic ranking applied to Indonesian job descriptions, with a focus on ranking coherence and contextual alignment rather than binary classification accuracy. The proposed framework transforms both user-entered skill keywords and job vacancy descriptions into dense vector representations within a shared embedding space. Semantic similarity is then computed using cosine similarity, enabling the system to rank job postings according to their contextual proximity to the user query. The recommendation output is presented in a Top-N format, prioritizing vacancies with the highest semantic correspondence. Experiments conducted on a dataset of 523 job postings demonstrate that the system consistently produces semantically coherent ranking patterns, where vacancies emphasizing relevant competencies are positioned at higher ranks. Qualitative evaluation further indicates stable ranking behavior across repeated queries, suggesting robustness in similarity-based ordering. These findings support the feasibility of embedding-based semantic retrieval as a practical and interpretable solution for content-driven job recommendation in dynamic digital recruitment environments.]]>
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