Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data-Reference-Cited by-同舟云学术

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data

Published:2023-12-08 Issue: Volume: Page:1-5
ISSN:
Container-title:2023 Eighth International Conference on Informatics and Computing (ICIC)
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Author:

Andri ¹,Yunis Roni²,Tanti ³

Affiliation:

1. Universitas Mikroskil,Undergraduate Program of Computer Science Department,Medan,Indonesia

2. Universitas Mikroskil,Undergraduate Program of Information Systems Department,Medan,Indonesia

3. Universitas Mikroskil,Undergraduate Program of Information Technology Department,Medan,Indonesia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10381889/10381891/10382055.pdf?arnumber=10382055

Reference16 articles.

1. The problem with higher education student completions in the global north

2. A graphically based machine learning approach to predict secondary schools performance in Tunisia

3. Evolutionary machine learning builds smart education big data platform: Data-driven higher education

4. An Explainable Machine Learning Approach for Student Dropout Prediction

5. Predicting Louisiana Public High School Dropout through Imbalanced Learning Techniques

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