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Penelitian ini mengembangkan sistem screening CV otomatis berbasis web yang mengintegrasikan Natural Language Processing (NLP) dan pendekatan rule-based untuk mendukung proses rekrutmen Human Resources. Sistem dibangun dengan Python menggunakan Gradio untuk antarmuka, PyMuPDF untuk ekstraksi teks PDF, spaCy untuk Named Entity Recognition, serta Regular Expressions untuk pencocokan pola keahlian. Model weighted scoring menilai pengalaman kerja, hard skill wajib, dan hard skill tambahan untuk mengklasifikasikan kandidat menjadi Utama, Dipertimbangkan, dan Tidak Cocok dengan ambang skor 75, 50, dan 0 sambil menjaga oversight manusia.",{"@graph":51,"@context":100},[52,68,83],{"@type":53,"itemListElement":54},"BreadcrumbList",[55,59,62,65],{"item":56,"name":57,"@type":58,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":60,"name":10,"@type":58,"position":61},"https://docshare.wps.com/id/template/",2,{"item":63,"name":31,"@type":58,"position":64},"https://docshare.wps.com/id/template/resume/",3,{"item":66,"name":47,"@type":58,"position":67},"https://docshare.wps.com/id/template/automated-cv-screening-system-based-on-nlp-and-rule-based-for-web-based-candidate-selection/237578/",4,{"url":66,"name":47,"@type":69,"author":70,"headline":47,"publisher":73,"fileFormat":76,"inLanguage":45,"description":49,"dateModified":77,"datePublished":77,"encodingFormat":76,"isAccessibleForFree":78,"interactionStatistic":79},"DigitalDocument",{"name":71,"@type":72},"Mabel","Person",{"url":56,"name":74,"@type":75},"DocShare","Organization","application/pdf","2026-09-11",true,{"@type":80,"interactionType":81,"userInteractionCount":4},"InteractionCounter",{"@type":82},"ViewAction",{"@type":84,"mainEntity":85},"FAQPage",[86,92,96],{"name":87,"@type":88,"acceptedAnswer":89},"Sistem screening CV otomatis ini menggunakan metode apa saja?","Question",{"text":90,"@type":91},"Sistem mengintegrasikan Natural Language Processing (NLP) untuk ekstraksi data dan pendekatan rule-based untuk pengambilan keputusan berbasis kriteria HR.","Answer",{"name":93,"@type":88,"acceptedAnswer":94},"Bagaimana model penilaian kandidat dihitung?",{"text":95,"@type":91},"Sistem memakai weighted scoring dengan tiga komponen: skor pengalaman (40%), skor hard skill wajib (40%), dan skor hard skill tambahan (20%).",{"name":97,"@type":88,"acceptedAnswer":98},"Kandidat diklasifikasikan ke kategori apa dan bagaimana ambang skornya?",{"text":99,"@type":91},"Hasil pengujian menunjukkan kandidat diklasifikasikan menjadi tiga kategori rekomendasi: Utama, Dipertimbangkan, dan Tidak Cocok, dengan threshold skor 75, 50, dan 0.","https://schema.org",{"og:url":66,"og:type":102,"og:title":47,"og:site_name":74,"og:description":49},"article",{"robots":104,"canonical":66},"index,follow",{"doc_id":106,"site_id":44},237578,1789113534,{"code":4,"msg":5,"data":109},{"doc_id":106,"user_id":110,"nickname":71,"user_avatar":111,"doc_module":9,"category_id":30,"category_name":31,"doc_title":47,"doc_description":49,"doc_content":112,"file_id":113,"file_url":114,"file_type":115,"file_size":116,"view_count":4,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":117,"language":118,"language_code":45,"site_id":44,"html_lang":45,"table_of_contents":119,"faqs":120,"seo_title":121,"seo_description":49,"update_tm":107,"read_time":122},7971474920318,"https://ap-avatar.wpscdn.com/avatar/74000ee537e6b0ca360?x-image-process=image/resize,m_fixed,w_180,h_180&k=1788430171632181674","SISTEM SCREENING CVOTOMATIS BERBASIS NLP DAN RULE-BASED UNTUK  \nSELEKSIKANDIDAT BERBASIS WEB  \nRikardo1, Rafi Maulana Ash Shidqi2, Ana Sujana3, Sigit Wibawa4, Untung Rohwadi5,  \nFelix Wuryo Handono6  \nEmail: [rikardoo2212@gmail.com](rikardoo2212@gmail.com1)[1](rikardoo2212@gmail.com1), [rafi.maulanash@gmail.com](rafi.maulanash@gmail.com2)[2](rafi.maulanash@gmail.com2), [anasujana99@gmail.com](anasujana99@gmail.com3)[3](anasujana99@gmail.com3), [sigit.stb@bsi.ac.id](sigit.stb@bsi.ac.id4)[4](sigit.stb@bsi.ac.id4), [untung.unr@bsi.ac.id](untung.unr@bsi.ac.id5)[5](untung.unr@bsi.ac.id5),  \n[felix@bsi.ac.id](felix@bsi.ac.id6)[6](felix@bsi.ac.id6)  \n1,2,3,4,5,6Universitas Bina Sarana Informatika  \nABSTRAK  \nPeningkatan volume pelamar dalam era digital menuntut otomatisasi proses seleksikandidat yang efisien, akurat, dan objektif. Penelitian ini mengembangkan sistem screening CV otomatis berbasis web yang mengintegrasikan Natural Language Processing (NLP) dan pendekatan rule-based untuk meningkatkan efisiensi prosesrekrutmen di divisi Human Resources. Sistem dirancang menggunakan Python denganmemanfaatkan pustaka Gradio untuk antarmuka pengguna, PyMuPDF untuk ekstraksiteks PDF, spaCy untuk Named Entity Recognition, dan Regular Expressions untuk pencocokan pola keahlian. Algoritma hybrid menggabungkan ekstraksi otomatis data kritis (nama, pengalaman kerja, keterampilan) melalui NLP dengan penilaian berbasis aturan eksplisit yang ditentukan HR. Sistem menggunakan weighted scoring model dengan tiga komponen utama: skor pengalaman (40%), skor hard skill wajib (40%), dan skor hard skill tambahan (20%) . Pengujian pada data simulasi menunjukkan sistem mampu mengklasifikasikan kandidat menjadi tiga kategori rekomendasi (Utama, Dipertimbangkan, Tidak Cocok) dengan threshold skor 75, 50, dan 0. Hasil penelitian menunjukkan sistem berhasil mengotomatisasi proses seleksi awal, mengurangi waktu screening manual, meningkatkan konsistensi penilaian, serta memberikan dasar objektif untuk pengambilan keputusan rekrutmen sambil tetap mempertahankan oversight manusia dalam penilaian akhir.  \nKata Kunci: Screening CV Otomatis, Natural Language Processing, Rule-Based, Sistem Rekomendasi, Rekrutmen Berbasis AI.  \nABSTRACT  \nThe increasing volume of job applicants in the digital era demands automation of the selection process to ensure efficiency, accuracy, and objectivity. This research develops  \nan automated résumé screening system based on web technologies. It integrates Natural Language Processing (NLP) and rule-based approaches to enhance Human Resources recruitment efficiency. The system uses Python, Gradiofor the user interface, PyMuPDF for PDF text extraction, spaCy for Named Entity Recognition, and Regular Expressions for skill pattern matching. A hybrid algorithm extracts critical data (name, work experience, skills) with NLP and applies HR-determined scoring rules. The scoring model weights experience (40%), mandatory hard skills (40%), and additional hard skills (20%). Testing on simulated data shows that the system classifies candidates into Primary, Considered, and Not Suitable, with score thresholds of 75, 50, and 0, respectively. Results indicate successful automation of initial selection, reduced screening time, improved consistency, and greater objectivity. Human oversight is maintained during final evaluations.  \nKeywords: Automated CV Screening, Natural Language Processing, Rule-Based, Recommendation System, AI-Driven Recruitment.  \n1. PENDAHULUAN  \nPerkembangan dunia kerja di era digital telah mendorong perusahaan untuk beradaptasi dengan teknologi dalam setiap aspek rekrutmen. Salah satu tantangan utama HRD adalah meningkatnyajumlah pelamar yang menyebabkan proses penyeleksian CV secara manual menjadi sangat memakan waktu, rentan kesalahan, dan berpotensimenimbulkan bias subjektif. Kebutuhan akan otomatisasi dalam seleksi awal kandidatkini menjadi semakin mendesak seiring tuntutan efisiensi dan akurasi proses rekrutmen yang lebih ba","cbCaipD4KSvdFMwh","https://ap.wps.com/l/cbCaipD4KSvdFMwh","pdf",277330,15,"Indonesian","# Pendahuluan\n## Latar belakang tantangan rekrutmen di era digital\n## Peran NLP dalam ekstraksi informasi CV\n## Integrasi rule-based untuk pengambilan keputusan\n## Studi terkait dan perbandingan pendekatan penelitian","[{\"question\":\"Sistem screening CV otomatis ini menggunakan metode apa saja?\",\"answer\":\"Sistem mengintegrasikan Natural Language Processing (NLP) untuk ekstraksi data dan pendekatan rule-based untuk pengambilan keputusan berbasis kriteria HR.\"},{\"question\":\"Bagaimana model penilaian kandidat dihitung?\",\"answer\":\"Sistem memakai weighted scoring dengan tiga komponen: skor pengalaman (40%), skor hard skill wajib (40%), dan skor hard skill tambahan (20%).\"},{\"question\":\"Kandidat diklasifikasikan ke kategori apa dan bagaimana ambang skornya?\",\"answer\":\"Hasil pengujian menunjukkan kandidat diklasifikasikan menjadi tiga kategori rekomendasi: Utama, Dipertimbangkan, dan Tidak Cocok, dengan threshold skor 75, 50, dan 0.\"}]","SISTEM SCREENING CV OTOMATIS BERBASIS NLP DAN RULE-BASED UNTUK SELEKSI KANDIDAT BERBASIS WEB | PDF",5]