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All Journal CCIT (Creative Communication and Innovative Technology) Journal
Destanto, Tri
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Computer Science & IT

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Comparison of Data Mining for Classifying Student Graduation Levels Using Naive Bayes, Decision Tree, and Random Forest Methods (Case Study of The Undergraduate Program at Mitra Indonesia University) Destanto, Tri; Nugroho, Handoyo Widi
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3409

Abstract

This study aims to apply data mining techniques to classify student graduation rates in the Undergraduate Program at Mitra Indonesia University. The methods used in this study include Naive Bayes, Decision Tree, and Random Forest. The data used includes student academic data, such as grades, attendance, and other demographic information. The research steps include data collection, data cleaning, data analysis, and the application of data mining algorithms. The results of the study show that the Random Forest method provides the highest accuracy compared to Naive Bayes and Decision Tree in predicting student graduation rates. The Random Forest method achieved an accuracy of 85%, while the Decision Tree achieved 80%, and Naive Bayes achieved 75%. These findings are expected to help Mitra Indonesia University identify students at risk of not graduating on time, so appropriate interventions can be provided to improve graduation rates
Implementation of Data Mining for Classifying Student Graduation Levels Using Naive Bayes, Decision Tree, Random Forest, Support Vector Machines and Neural Networks Methods (Case Study of The Undergraduate Program at Mitra Indonesia University) Hartanto, M. Budi; Destanto, Tri; Yuniarthe, Yodhi; Winarko, Triyugo
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3441

Abstract

This study aims to classify student graduation levels using five data mining methods: Naive Bayes, Decision Tree, Random Forest, Support Vector Machines, and Neural Networks. Conducted as a case study at Mitra Indonesia University, the research utilizes academic data, including GPA, course completion rates, and attendance records, to predict graduation success. The results reveal that Random Forest and Neural Networks exhibit the highest accuracy, making them the most suitable methods for predicting student outcomes. These findings contribute to the development of early intervention programs for students at risk of delayed graduation, providing valuable insights for higher education institutions.
Co-Authors handoyo widi nugroho M. Budi Hartanto, M. Budi Triyugo Winarko, Triyugo Yodhi Yuniarthe