Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia

Research Article

Student's Skills Competency Test Prediction Using C4.5 Algorithm

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  • @INPROCEEDINGS{10.4108/eai.12-10-2019.2296548,
        author={Ultach  Enri and Jajam Haerul Jaman and Muhammad Rizky Ananda},
        title={Student's Skills Competency Test Prediction Using C4.5 Algorithm},
        proceedings={Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia},
        publisher={EAI},
        proceedings_a={MSCEIS},
        year={2020},
        month={7},
        keywords={c45 algorithm data mining feature selection},
        doi={10.4108/eai.12-10-2019.2296548}
    }
    
  • Ultach Enri
    Jajam Haerul Jaman
    Muhammad Rizky Ananda
    Year: 2020
    Student's Skills Competency Test Prediction Using C4.5 Algorithm
    MSCEIS
    EAI
    DOI: 10.4108/eai.12-10-2019.2296548
Ultach Enri1,*, Jajam Haerul Jaman1, Muhammad Rizky Ananda1
  • 1: Informatics Engineering Program Faculty of Computer Science Universitas Singaperbangsa Karawang
*Contact email: ultach@staff.unsika.ac.id

Abstract

Competency Skills (UKK) is part of government intervention in ensuring the quality of education in the Secondary Vocational School aims to measure the achievement of a certain level of competency in students' appropriate competency skills. The purpose of the research is to find out how competent the students' in their vocation as well as new strategies for educators in providing more effective learning by using C4.5 algorithm combining with feature selection. Overall the results of the validation of the model experiments that have the best influence are supplied with a set of test accuracy values of 96.875%, and 13 optimal attributes are selected. Therefore, in this study, the C4.5 algorithm with feature selection can provide good and effective results.