<![CDATA[The rapid development of technology generates information about job vacancies every day, creating a dynamic ecosystem that has changed the employment landscape with the emergence of new positions related to new technologies. Therefore, understanding job position trends is crucial for applicants to anticipate market needs and prepare accordingly. Position trend classification is the process of identifying and analyzing job titles into categories that are currently popular. This research aims to create a classification model using the Naive Bayes method in grouping job vacancy titles by category. Naive Bayes is a probabilistic-based prediction technique based on Bayes' Theorem, but researchers use TF-IDF text representation parameters to calculate the weight of words in the job title dataset. Data was taken from two job search platforms, jobs.linkedin.com and jobstreet.com, which resulted in seven categories from 7,254 datasets. Classification results using the naive bayes method provide good results with an accuracy rate of 82.77%. Then the test results obtained with a ratio of 8: 2 produce an average F1-Score value of 82.78%.]]>
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