Integrating TOPSIS-KNN Classifier for Job Market Trends Identification and Employability Prediction

Abstract

This paper addresses the growing importance of identifying job market trends and assessing graduate employability amidst technological advancements and market changes. It highlights the function of artificial intelligence, including machine learning and deep learning, in providing adaptive solutions to complex unemployment issues. Key questions explored include methods for identifying trends, criteria for classification, and selection of suitable MCDM methods like AHP and ELECTRE, with TOPSIS identified as optimal. The study also evaluates ten ML models based on accuracy and other metrics to forecast employability and enhance workforce readiness, offering insights for policy-making and educational strategies. The originality of this article lies in the fact that it is the first time an article has merged the two MCDM and ML methods for predicting employability issue, which, in the end, provided satisfying results for the identification of market trends and the prediction of employability.