SKRIPSI DIGITAL
Sistem Rekomendasi Resume Screening Dan Candidate Ranking Menggunakan Natural Language Processing (NLP) Pada Job Portal Berbasis Web = RESUME SCREENING AND CANDIDATE RANKING RECOMMENDATION SYSTEM USING NATURAL LANGUAGE PROCESSING (NLP) ON WEB-BASED JOB PORTALS
An effective and efficient candidate selection process is a key factor in improving recruitment quality in the digital age. Conventional methods of resume screening are often time-consuming and prone to human bias. This study developed a resume screening and candidate ranking optimization system based on Natural Language Processing (NLP) on a web-based job portal. This system automates the process of information extraction, skill analysis, and candidate scoring using the Sentence – BERT (Bidirectional Encoder Representations from Transformers) model. Resume data is converted into raw text, then undergoes cleaning and tokenization processes to remove noise. The developed model can identify skills, experience, and relevance to job descriptions. Research results show that this system can increase selection speed by up to 70% and improve candidate selection accuracy by up to 73% compared to traditional methods. With this system, the recruitment process becomes more measurable, transparent, and accurate, helping companies objectively find the most suitable candidates.
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Karya Umum 
Filsafat 
Agama 
Ilmu-ilmu Sosial 
Bahasa 
Ilmu-ilmu Murni 
Ilmu-ilmu Terapan 
Kesenian, Hiburan, dan Olahraga 
Kesusastraan 
Geografi dan Sejarah