Enhancement Of The C4.5 Decision Tree Algorithm With Anova For Predicting Academic Achievement Of Students At Smpn.16 Kota Jambi - Osviarni, Rice; Assegaff, Setiawan; Jasmir, Jasmir; Nurhadi, Nurhadi (Informatika, Universitas Jenderal Soedirman, 2026)
This study aims to improve the accuracy of predicting student academic achievement by integrating the Analysis of Variance (ANOVA) method with the C4.5 Decision Tree algorithm. In the context of information systems, this research holds significant importance for the development of more reliable Decision Support Systems (DSS) or early warning systems in school environments. The research was conducted at SMPN 16 Jambi City using secondary data from three academic years (2022/2023-2024/2025) covering academic variables, attendance, and parental income. The main issue addressed was the limitations of the C4.5 algorithm in handling irrelevant features and unbalanced data, which, at the system implementation level, can lead to inaccurate recommendations or alerts.This research method employed a data mining approach with stages including data cleaning, numeric conversion, missing value imputation, formation of derived variables, and categorization of the target variable