Joint Workshop KO2PI and The 1st International Conference on Advance & Scientific Innovation

Research Article

Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine

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  • @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
Muhammad Dharma Tuah Putra Nasution1,*, Andysah Putera Utama Siahaan2, Yossie Rossanty1, Solly Aryza2
  • 1: Faculty of Social Science, Universitas Pembangunan Panca Budi, Medan, Indonesia
  • 2: Faculty of Science and Technology, Universitas Pembangunan Panca Budi, Medan, Indonesia
*Contact email: dharma_nasution@dosen.pancabudi.ac.id

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.