Development of Hanapbuh.AI: An AI-Powered Semantic Resume Matching System for Filipino Job Seekers
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The Philippine online job market is characterized by information overload and a heavy reliance on keyword-based search mechanisms that routinely fail to capture semantic skill alignment between candidates and available positions. This mismatch imposes substantial search costs on job seekers—particularly students and fresh graduates—and perpetuates a structural skills gap within the local labour market. This study aimed to design, develop, and evaluate HanapBuh.AI, a candidate-centric web application that automates resume parsing and performs semantic job matching to surface contextually relevant employment opportunities for Filipino job seekers.
Employing a descriptive-developmental research design, the study integrated spaCy (en_core_web_md) for named-entity recognition and the Sentence Transformer model (all-MiniLM-L6-v2) for 384-dimensional semantic embedding and cosine-similarity matching. The system was evaluated by n = 52 Filipino job seekers and fresh graduates using an instrument adapted from the ISO/IEC 25010 Software Quality Model across eight quality dimensions on a four-point Likert scale. Additional automated quality assurance was conducted via Apache JMeter, Google Lighthouse, and Playwright. The system achieved an overall General Weighted Average (GWA) of 3.76 out of 4.00 ("Strongly Agree"), with Usability (M = 3.80, SD = 0.45) as the highest-rated dimension, followed by Security (M = 3.77, SD = 0.49) and Portability (M = 3.77, SD = 0.48). Load testing confirmed a 0.00% error rate across 200 requests, and automated functional testing via Playwright recorded a 100% pass rate for all core user journeys. HanapBuh.AI effectively bridges the gap between candidate qualifications and publicly available job listings by replacing unguided keyword browsing with transparent, meaning-based matching. The system meets ISO/IEC 25010 quality thresholds and is technically validated for real-world deployment, constituting a viable proof-of-concept for semantic recruitment technology localized to the Philippine labor market.
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