Analysis and Development of Job Vacancies Using Web-Based SAW and TOPSIS Methods

Abstract

The rapid development of information technology has increased the availability of online job vacancy information; however, this condition often makes it difficult for job seekers to select suitable jobs according to their criteria and preferences. This study aims to analyze and develop a web-based job vacancy recommendation system using the Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The SAW method is applied to calculate initial preference values based on weighted criteria, while the TOPSIS method is used to determine the final ranking of job vacancies based on their closeness to the ideal solution. The system is developed using the Extreme Programming approach and tested through functional black box testing. The results indicate that the proposed recommendation system is able to provide objective, structured, and relevant job vacancy recommendations according to user preferences. The integration of SAW and TOPSIS effectively improves the quality of job ranking and supports job seekers in making better decisions