Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm

Authors

  • Sinta Rukiastiandari Universitas Bina Sarana Informatika
  • Luthfia Rohimah Universitas Bina Sarana Informatika
  • Aprillia Universitas Bina Sarana Informatika
  • Fara Mutia Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31294/p.v26i1.3165

Keywords:

Decission Tree, Genethic Algorithm, Graduation

Abstract

This research aims to improve the accuracy of predicting student permit results in the digital era by utilizing machine learning techniques. The main focus is the use of a Decision Tree (DT) model optimized with a Genetic Algorithm (GA) to overcome the limitations of accuracy and testing of conventional methods. This research began with collecting student academic data, followed by preprocessing to eliminate incompleteness and organize the data format. The DT model is then built and optimized with GA, which is inspired by biological evolutionary processes to improve feature selection and parameter tuning. The results show a significant increase in prediction accuracy, from 86.19% to 87.68%, and an increase in the Area Under Curve (AUC) value from 0.755% to 0.788%. This research not only proves the effectiveness of GA integration in improving DT models, but also paves the way for the application of evolutionary techniques in educational data analysis and other fields. The main contributions of this research include the development of more accurate prediction models and practical applications in educational contexts, with the hope of assisting educational institutions in making more informed decisions for their students.

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Published

2024-03-29

How to Cite

Rukiastiandari, S., Rohimah, L., Aprillia, A., & Mutia, F. (2024). Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm. Paradigma - Jurnal Komputer Dan Informatika, 26(1), 1-6. https://doi.org/10.31294/p.v26i1.3165

Issue

Vol. 26 No. 1 (2024): March 2024 Period

Section

Articles

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Copyright (c) 2024 Sinta Rukiastiandari, Luthfia Rohimah, Aprillia, Fara Mutia

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