Implementation K-Means Clustering Method in Job Recommendation System

Work is important for everyone to earn income. With the large number of new graduates each year, finding job vacancies is a problem for students who have just completed their studies in higher education because they still do not have work experience so they are required to look for jobs that really match their criteria. Applications made can recommend specific job vacancies for undergraduates from universities (undergraduates) with the K-Means Clustering method. Applications in the form of websites that become third parties for companies and applicants. This application is one of the means that can provide solutions to companies and applicants in finding workers or jobs using a recommendation system. The problem to be studied is how to apply the K-Means Clustering method to the job vacancy recommendation system. The recommendation system in this application will calculate the level of match of the applicant’s main skills, salary, location, and other skills with the needs of the company. The stages of making a recommendation system are making system designs and designs which include context diagrams, DFD, ERD and interface design. built with PHP, Java, jQuery, JavaScript, HTML, and CSS. Program testing is done by black box testing method. The weight given shows that the K-Means Clustering method can be applied to the job vacancy recommendation system and can display job recommendations according to the applicant’s personal data. Questionnaire testing is given to applicants, companies, and admins with elements of testing based on user satisfaction, user convenience and system quality, resulting in the conclusion that the system can run well by getting a percentage of 87.6%.