000			02948nam a22003257a 4500
003			OSt
005			20250711125421.0
008			250711b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH T 58.5 _bA43 2025
100			_aAlcantara, John Paulo C. _eauthor
245			_aHire support: _bresume verification and applicant ranking system using natural language processing/ _cJohn Paulo C. Alcantara, Alexis Glenn A. Aspiras, Princess Nichole B. Espinosa, Abbegail Miles Leonen, and Laica D. Ygot.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_ax, 143pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Science.-- _bBachelor of science in computer science: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aYear by year, the job market has become more competitive, with numerous applicants submitting their resumes across multiple platforms, causing companies to invest significant time in screening resumes and assessing applicant qualifications. This situation is further worsened by resume deception and embellishment issues, creating unfairness among applicants and potential losses for companies. To address these challenges, this study developed 'Hire Support: Resume Verification and Applicant Ranking System using Natural Language Processing,' a web-based application that used NLP to help Human Resource personnel and companies by verifying resumes through supporting documents and ranking applicants based on company-defined criteria. In the web application, applicants can apply for positions from various registered companies, while companies can view applicants' score summaries across various criteria accompanied by resume verification results. The system was developed using Python, Django, and PostgreSQL for the web application, alongside Python, Tesseract OCR, SpaCy, and BERT Base Case Model for the resume verification and scoring model. Evaluation results demonstrate that the system is fully functional with compliance with ISO/IEC 25010 standards, achieving a weighted mean of 3.52, described as "Highly Acceptable" by thirty (30) respondents. The developed system efficiently automates the resume screening process, significantly reducing manual effort while enhancing credibility through verification processes, thereby supporting companies in making informed decisions in identifying the most qualified applicant.
650			_aNLP integration
650			_aResume deception
651			_aPython backend
700			_aAspiras, Alexis Glenn A. _eauthor
700			_aEspinosa, Princess Nichole B. _eauthor
700			_aLeonen, Abbegail Miles _eauthor
700			_aYgot, Laica D. _eauthor
942			_2lcc _cBTH COS _n0
999			_c30298 _d30298