MACHINE LEARNING TO CREATE DECISION TREE MODEL TO PREDICT OUTCOME OF ENTERPRENEURSHIP PSYCHOLOGICAL READINESS (EPR)

Nesi Syafitri; Syarifah  Farradinna; Wella  Jayanti; Yudhi Arta

doi:10.52436/1.jutif.2023.4.2.590

Nesi Syafitri Teknik Informatika, Fakultas Teknik, Universitas Islam Riau, Indonesia
Syarifah Farradinna Psikologi, Fakultas Psikologi, Universitas Islam Riau, Indonesia
Wella Jayanti Psikologi, Fakultas Psikologi, Universitas Islam Riau, Indonesia
Yudhi Arta Teknik Informatika, Fakultas Teknik, Universitas Islam Riau, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.2.590

Keywords: Accuracy, Decision Tree, Enterprenuer Readiness, Machine Learning, Psychological assessment

Abstract

This study aims to create a decision tree model using machine learning to predict psychological readiness for entrepreneurship in college graduates. This research was conducted through several stages of research. In the early stages, a survey was conducted on 700 students from several universities in Riau aged between 17-25 years. The survey was conducted using the Entrepreneur Psychology Readiness (EPR) instrument. Furthermore, the survey data was validated and obtained 604 valid data to be used in forming machine learning models The urgency of this research is to find a number of decision rules from the best decision tree model to be used in building AI-based counseling applications in measuring entrepreneurial psychology readiness for college graduates. In this research, the decision tree model that is formed is divided into 2 models, namely: decision tree with pruning model and decision tree with unpruning. The pruning decision tree model produces 180 decision rules, while the unpruning model produces 121 decision rules. Good accuracy results are obtained in the pruned decision tree, which is above 99% in the use training set mode, and 82.87% in the percentage split mode. Meanwhile, the accuracy results on the unpruned decision tree are 90.18% with the use training set mode test, and 80.38% in the percentage split mode. The decision tree model with pruning technique has better performance than the unpruning decision tree model.

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