Analisis Multidimensional Profil Mahasiswa Studi Kasus : Program Studi Sistem Informasi UKDW


  • Agustinus Arsa Paskha A,  Program Studi Sistem Informasi, Universitas Kristen Duta Wacana
  • Yetli Oslan,  Program Studi Sistem Informasi, Universitas Kristen Duta Wacana
  • Lussy Ernawati,  Program Studi Sistem Informasi, Universitas Kristen Duta Wacana

(*) Corresponding Author



Machine Learning, Support Vector Machine, klasifikasi, kategori mahasiswa


In teaching and learning activities, analysis of students is needed. This is done to determine the proper way of learning. The right way of learning can increase the motivation of students. If the motivation of students increases, the academic value will also increase. One way that can be done is to classify students based on predetermined categories. The UKDW SI Study Program does not yet have a system that can classify the categories of students. This research was conducted to answer the above problems. Then machine learning will be built, which can automatically determine the categories of students. The method used to classify students is the Support Vector Machine (SVM). SVM has the advantage that it can be applied to cases that have high dimensions. The conclusion from this research is that the SVM method is very appropriate to be implemented in this study. This is evident in the machine learning model accuracy test on the system, which is 92.3%. With the existence of machine learning to classify students, teachers make it easier to do analysis. So that it is expected to provide an overview of the appropriate learning methods to be applied to students.


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How to Cite

A. Arsa Paskha A, Y. Oslan, and L. Ernawati, “Analisis Multidimensional Profil Mahasiswa Studi Kasus : Program Studi Sistem Informasi UKDW”, JUTEI, vol. 6, no. 1, pp. 1–10, Apr. 2022.