Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad, Khyrina Airin Fariza Abu Samah and Nuwairah Aimi Ahmad Kushairi

The recruitment process is vital for organizations. In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. Recruiting unqualified applicants may affect the organization. The manual recruitment process entails significant time, high costs, and potential bias. Thus, this project aims to classify and generate a list of potential job applicants by analyzing various attributes of their LinkedIn accounts, such as title, location, skills, education, language, certification, and years of experience. This project implements the Naive Bayes algorithm as the classification algorithm. The classification is set to two categories: Eligible or Ineligible. The NB model achieved a commendable accuracy of 89.8%, indicating good performance in classifying potential job applicants. The system’s functionality based on the use case and usability tested by the Human Resources expert has been tested to evaluate its system requirements. The usability testing yields a score of 80% which indicates that the system is acceptable. The classification results are visualized, allowing users to identify eligible applicants efficiently. Thus, this project can help users find suitable applicants for the job. Future research can expand the criteria to include cultural fit and behavior traits.