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Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out

Authors

  • I Gusti Made Wahyu Krisna Widiantara Universitas Pendidikan Ganesha
  • Kadek Yota Ernanda Aryanto Universitas Pendidikan Ganesha
  • I Made Gede Sunarya Universitas Pendidikan Ganesha

DOI:

10.47709/brilliance.v3i2.3155

Keywords:

Artificial Neural Network, Classification, Dropouts, Learning Vector Quantization, Unbalanced Data

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Abstract

Universities, as providers of academic education services, are required to provide an optimal educational process for students to produce a generation of quality human beings. Student learning success is seen as the success of universities in implementing the higher education process. One of the problems universities face in maintaining the quality of education is student dropout. The high dropout rate in universities can impact accreditation assessments. As a result, it will affect the level of public trust. The number of dropouts in higher education can be minimized from an early age by analyzing the factors that cause student dropouts using data on students who graduated and those who dropped out. This data can be used to determine student dropout patterns by classifying them using the artificial neural network learning vector quantization (LVQ) approach. The data used in this research was 4053, consisting of 3840 graduate student data and 213 dropout student data. This data is considered unbalanced, an unbalanced dataset can cause errors because the model tends to classify the majority class with a high classification and pays less attention to the minority class. So, it is necessary to apply oversampling techniques to overcome this problem. The research results show that the application of the LVQ method to unbalanced data produces an accuracy value of 95.53%, a precision value of 100%, a recall value of 15.02% and an f1-score of 0.26, while the application of the LVQ method to data that has undergone resampling resulting in an accuracy value of 94.66%, a precision value of 92.22%, a recall value of 97.55%, and an f1-score value of 0.95. The LVQ method can be used to classify dropout students with excellent results.

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ARTICLE Published HISTORY

Submitted Date: 2023-11-08
Accepted Date: 2023-11-08
Published Date: 2023-11-17

How to Cite

Widiantara, I. G. M. W. K., Aryanto, K. Y. E. ., & Sunarya, I. M. G. . (2023). Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out. Brilliance: Research of Artificial Intelligence, 3(2), 262-269. https://doi.org/10.47709/brilliance.v3i2.3155