Pengembangan Sistem Rekomendasi Pekerjaan di Linkedin Berbasis CV Pengguna Menggunakan Metode Content-Based Filtering

Armadianti, Wanda (2025) Pengembangan Sistem Rekomendasi Pekerjaan di Linkedin Berbasis CV Pengguna Menggunakan Metode Content-Based Filtering. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan teknologi telah mengubah media sosial menjadi alat yang sangat penting dalam berbagai aspek kehidupan, termasuk komunikasi, hiburan, dan pendidikan. Di dunia profesional, platform seperti LinkedIn memainkan peran vital dalam branding pribadi, perekrutan, dan pengembangan karir. Namun, fitur pencarian pekerjaan di LinkedIn sering kali terbatas dalam memberikan rekomendasi yang relevan dan personal karena kompleksitas informasi, variasi kebutuhan pekerjaan, serta ketidaksesuaian antara deskripsi pekerjaan dan profil pengguna. Penelitian ini bertujuan mengembangkan sistem rekomendasi pekerjaan berbasis Content-Based Filtering menggunakan Curriculum Vitae (CV) pengguna sebagai sumber utama. Tiga metode vektorisasi teks dibandingkan, yaitu TF-IDF, Word2Vec, dan Sentence-BERT, dengan evaluasi performa menggunakan metrik Normalized Discounted Cumulative Gain (NDCG), validasi oleh pakar, serta user testing. Hasil menunjukkan bahwa Word2Vec adalah model paling optimal dengan skor NDCG tertinggi (0,971), diikuti oleh SBERT (0,909) dan TF-IDF (0,550). Word2Vec dinilai paling relevan dan konsisten oleh pakar maupun responden, sementara SBERT unggul dalam menyesuaikan rekomendasi dengan minat karir. Sistem yang dikembangkan juga telah diimplementasikan dalam bentuk website yang dinilai cukup intuitif dan mudah digunakan oleh mayoritas pengguna. Penelitian ini menunjukkan bahwa sistem rekomendasi berbasis CV dengan Word2Vec memiliki potensi besar dalam meningkatkan relevansi pencarian kerja di LinkedIn dan membantu pengguna menemukan peluang karir yang sesuai dengan keahlian dan minat mereka.
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Technological advancements have transformed social media into a crucial tool across various aspects of life, including communication, entertainment, and education. In the professional world, platforms like LinkedIn play a vital role in personal branding, recruitment, and career development. However, LinkedIn’s job search feature often falls short in providing relevant and personalized recommendations due to the complexity of available information, varied interpretations of job requirements, and misalignment between job descriptions and user profiles. This study aims to develop an effective job recommendation system on LinkedIn using users’ Curriculum Vitae (CV) through a Content-Based Filtering approach. Three text vectorization methods—TF-IDF, Word2Vec, and Sentence-BERT—were compared, with performance evaluated using the Normalized Discounted Cumulative Gain (NDCG) metric, expert validation, and user testing. The results show that Word2Vec is the most optimal model, achieving the highest NDCG score (0,971), followed by SBERT (0,909) and TF-IDF (0,550). Word2Vec was consistently rated as the most relevant and accurate by both experts and users, while SBERT performed better in aligning with users’ career interests. The recommendation system was implemented as a website, which was considered intuitive and user-friendly by most respondents. This research demonstrates that a CV-based recommendation system using Word2Vec has strong potential to enhance job search relevance on LinkedIn and help users discover career opportunities that align more closely with their skills and interests.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Content-Based Filtering, Curriculum Vitae, LinkedIn, Sistem Rekomendasi, Recommendation System
Subjects:	Q Science > QA Mathematics > QA76.9.I58 Recommender systems (Information filtering)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Wanda Armadianti
Date Deposited:	18 Jul 2025 07:02
Last Modified:	18 Jul 2025 07:02
URI:	http://repository.its.ac.id/id/eprint/120055

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