Efficient job matching: an automated system for credential evaluation and position recommendation using fuzzy logic/ Shanika Maureen D. Barbosa, Jalen Andrei P. Fajardo, Jhon Philip H. Guiang, Jolo Czar Ken DV. Nario, and Emmanuel P. Ruiz Jr.--

Bachelor's thesis

College Of Science.--
Bachelor of science in computer science:
Technological University of the Philippines,
2025.

Includes bibliographic references and index.

The study developed an automated system for credential evaluation and position
recommendation using the fuzzy logic algorithm to improve the recruitment process.
Because it is often hard for applicants to know which job best fits their background, many
end up applying to roles that do not match their skills or experience. With the help of
technology, The researchers can now create a system that reads their curriculum vitae and
suggests suitable positions, making the process easier not just for the applicants but also
for the recruitment team. Applicants can upload their curriculum vitae, along with details
like education, qualifications, skills, certifications, work experience, and supporting
documents in PDF or DOCX format, directly into the system for evaluation. The system
uses PyMuPDF to parse documents and extract text. It then processes the files to pull out
key details from the applicant’s credentials, which are cross-checked against the data
manually entered by the user for added accuracy. The system uses Semantic Similarity
(NLP) to assess the compatibility between the requirements of the job and the
qualifications of the applicant for the chosen job listing. After the assessment, it
organizes the information of the applicant into recognizable categories and computes the
suitability score. To evaluate the applicant’s compatibility with the chosen job, the score
is analyzed using fuzzy logic. It then suggests job opportunities that correspond with the
applicant’s profile, based on the field or specialization. The system is developed using
web technologies, including Next.js (React) for both front-end and back-end
development, Node.js as the runtime environment, PostgreSQL for database
management, Flask for API handling, and AWS S3 bucket for cloud based file storage.
The system’s functionality and reliability were tested following the ISO 25010 for
Software Quality Standards. The result showed that the System is functional and usable,
with an overall weighted mean of 4.10 or a “Highly Acceptable” rating from the twenty
six (26) respondents.