Research Article
Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine
@INPROCEEDINGS{10.4108/eai.23-4-2018.2277587, author={Muhammad Dharma Tuah Putra Nasution and Andysah Putera Utama Siahaan and Yossie Rossanty and Solly Aryza}, title={Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine}, proceedings={Joint Workshop KO2PI and The 1st International Conference on Advance \& Scientific Innovation}, publisher={EAI}, proceedings_a={ICASI}, year={2018}, month={7}, keywords={prediction self-efficacy entrepreneurship passion svm}, doi={10.4108/eai.23-4-2018.2277587} }- Muhammad Dharma Tuah Putra Nasution
Andysah Putera Utama Siahaan
Yossie Rossanty
Solly Aryza
Year: 2018
Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine
ICASI
EAI
DOI: 10.4108/eai.23-4-2018.2277587
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.
