Machine Learning-Based Counseling to Predict Psychological Readiness for Aspiring Entrepreneurs

Machine learning has become an exciting topic in psychology-related research, one of which is counseling psychological readiness for entrepreneurship. An intelligent application developed using a machine learning model to assist the counseling process in measuring a person's psychological readiness for entrepreneurship. This application was generated using the Entrepreneurship Psychological Readiness (EPR) instrument. In this study, to get the most suitable machine learning model, a comparison of 2 (two) machine learning models, namely, Naïve Bayesian (NB) and k-Nearest Neighbor (k-NN), involving 1095 training data. There are 4 (four) prediction classes recommended from the results of counseling: categories not ready for entrepreneurship, given training, guided, and prepared for entrepreneurship. The EPR instrument consists of 33 question items to measure 8 (eight) parameters used as inputs for the prediction process. The data has been randomized, and the experiment has been repeated 5 (five) times to check the consistency of performance of all techniques. 80% of the data was used as training data, and the other 20% was used as testing data. The results of the five (5) trials show that the Naïve Bayesian model provides the most consistent results in predicting a person's psychological readiness for entrepreneurship, with 89.58% accuracy, in testing. Therefore, the Naïve Bayesian model is recommended to be used in psychological counseling to predict a person's readiness for entrepreneurship