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Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine

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  • Siahaan, Andysah Putera Utama

    (Universitas Pembangunan Panca Budi)

  • Nasution, Muhammad Dharma Tuah Putra

Abstract

This study discusses the prediction model of entrepreneurship intent for alumni. The data is obtained from the database of an online job market, alumni tracer and survey results to the alumni. This research applies the C4.5 decision tree algorithm to get a prediction model that shows the intention of entrepreneurship. Some essential indicators include Self-efficacy, Need for Achievement, Advisory Quotient, Locus of Control and Passion. The predictive model found that the best predictor was Self-efficacy which contributed to influence the entrepreneurship intention with a value of 79.7 percent. The authors recommend to educational institutions to foster candidate interest through curriculum improvement, field practice or learning models in and out of the classroom.

Suggested Citation

  • Siahaan, Andysah Putera Utama & Nasution, Muhammad Dharma Tuah Putra, 2018. "Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine," INA-Rxiv mznhb, Center for Open Science.
    • Handle: RePEc:osf:inarxi:mznhb
    DOI: 10.31219/osf.io/mznhb
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    Cited by:

    1. Josué Aarón López-Leyva & Miguel Ángel Ponce-Camacho & Alfredo Valadez-García & Víctor Manuel Ramos-García & Hania Nered Mena-Ibarra, 2022. "Entrepreneurship Intentions Analysis of Mexican University Students Using an Artificial Neural Network to Promote Sustainable Businesses: An Interdisciplinary Perspective," Sustainability, MDPI, vol. 14(4), pages 1-14, February.

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