In talent recruitment, companies seek talents who fit with their corporate culture among a large
number of applicants. To conduct this process, companies carry out psychological tests to assess the fitness of
applicants' personalities with their corporate culture. However, psychological testing requires cost and much
effort. Thus, an automated system is required to assist companies in the talent recruitment process that can
classify personalities through text and to reduce the effort needed. This research is conducted based on the
personality traits according to the corporate culture in Telkom Indonesia. The data used is text data that has been
labeled, pre-processed, and feature selected. The clean text data is used to create a classification model using
multinomial naive Bayes and decision trees. There are 6 models built based on 3 work cultures. The decision tree
achieves an accuracy of 33%, 66%, 80%, while multinomial naive Bayes with an accuracy of 83%, 50%, 60%,
which resulted as better performance.