Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization

Authors

  • Hafis Nurdin Universitas Nusa Mandiri, Indonesia
  • Sartini Universitas Nusa Mandiri, Indonesia
  • Sumarna Universitas Nusa Mandiri, Indonesia
  • Yana Iqbal Maulana Universitas Bina Sarana Informatika, Indonesia
  • Verry Riyanto Universitas Bina Sarana Informatika, Indonesia

DOI:

10.33395/sinkron.v8i4.12973

Keywords:

Student Graduation; Neural Networks; Particle Swarm Optimization; Prediction;

Abstract

In private universities in Indonesia, student graduation is something that is worth paying attention to, because it will be an aspect of the success of the university. Universities certainly have data on students who graduated, where student graduation data is very important to be taken into consideration by private universities, however with a lot of data it will make it difficult for private universities to find information from this data. Other researchers have previously carried out student graduation data with the same data by examining student graduation data using other methods. So we need a data mining algorithm that has never been tested on student graduation data. The method used is the neural network method with an optimization method, namely the particle swarm optimization method, to test the data, which will later produce information that is very useful for universities. After testing the student graduation data and getting accuracy results using the neural network method of 84.55% and after being optimized using the particle swarm optimization method, the accuracy results were optimal with a value of 86.94%. These results can be used by private universities to predict that students will graduate on time before they take their final semester so that the student graduation rate will be high.

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How to Cite

Nurdin, H., Sartini, S., Sumarna, S., Maulana, Y. I. ., & Riyanto, V. . (2023). Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2353-2362. https://doi.org/10.33395/sinkron.v8i4.12973

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

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Copyright (c) 2023 Hafis Nurdin, Sartini, Sumarna, Yana Iqbal Maulana, Verry Riyanto

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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