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Journal : bit-Tech

Job Vacancy Recommendation System Based on Text Description Analysis Using Word Embedding and Cosine Similarity Fathur Riski; Karina Auliasari; Mira Orisa
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3690

Abstract

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.
Co-Authors Achmad Abdillah, Nur Adi Pratama, Sena Agung Kurniawan Ahmad Fahrudi Setiawan AHMAD FAISOL Ahmad Faisol Alfaridzi, Febryan Ali Mahmudi Andriano Frans, Jemmy Aprilian Anarki, Galang Ardhi Nur Rasyid, Muhammad Arfan Ravy Wahyu Pratama, Mochamad Aria Alfaizi, Hafiz Ariwibisono, F.X Ariwibisono, FX Aryanto Lende, Junaedy Ashari, Muhammad Ibrahin Asyam Naufal, Kasih Aulisari, Karina Bagaskara Adhi P Budi Raharjo, Piter Deddy Rudhistiar Desmile, Janico Dzulfikar, Ahmad Eksi Adi Irawan, Alfonsus Ellio Dewa Alsveta, Aloysius Farid Riyan Wijaya, Aditya Fathur Riski Febriana Santi Wahyuni FX. Ariwibisono Habib Asiddiqie, Prananda Hani Zulfia Zahro Hidayatullah, Panji Ilham Syahriansya, Ali Izmi Apriosa, Silvia Jamil Nashrulloh Kadek Riski Dwi Putra, I Karina Auliasari Khoirunnisa Anggraini, Jihan Kukuh Prayogi, Panji Listyani Kartika, Angelina M. Ibrahim Ashari Mariza Kertaningtyas martha kusuma, edwin Maulana, Ferdian Maulidina, Vingki Indrayani Maureta, Sonia Michael Ardita Mirenty, Putri Mulan Mochammad Ibrahim Ashari Muhammad Rafi Faddilani Mukhlis Mukhlis Nizar Purwayana Nugraha, Ainin Novitasari, Gana Nur Hidayanti, Rahmah Nurina Nurina Nurlaily Vendyansyah Onny Setyawati Pambudi, Yitno Prasetiyo, Agung Sugih Prasetya, Rafid Artur Pratama, Rafif Pratama, Redo Primaswara P, Renaldi Prio Utomo, Yandi Pristiani, Tenti Purnomo Budi Santoso Putra Snyders, Saveraga Putu Agustini , Ni Qurrotuna, Fina Renaldi Prasetya Renaldi Primaswara Prasetya Rofila El Maghfiroh Samsaudin, Ruslin Satria Pradana, David Sentot Achmadi Septiardo, Pratedyo Surat Lelaona, Maria Avriliana Surya Putra, Johanes Suryo Adi Wibowo Taufik Hidayat Taufiq Rahmatullah, Muhammad Tri Pamungkas, Arum Vieri Agusliyanto, Naralda WAHYUDI, RIZAL Widi Wiguna, Chandra Xaverius Ariwibisono, Fransiscus Xaverius Ariwibisono, Fransiskus Yosep Agus Pranoto Yulio, Andi Yunita Sari, Hesti Zulfia Zahro’, Hani Zulkifli Abdillah