<![CDATA[This study aims to develop a classification system for high-achieving students by integrating academic and non-academic aspects using the Random Forest method. The main problem faced by Natal State High School 1 is that the process of identifying high-achieving students still focuses on academic grades and does not yet comprehensively incorporate other indicators such as discipline, attendance, and extracurricular activities. This study employs a quantitative approach with data collection techniques including observation, interviews, and literature review. The data used were derived from the report cards for the odd-semester of the 2024/2025 academic year, covering 222 eleventh-grade students. The research stages included data preprocessing (data cleaning, transformation, normalization, and feature selection), data splitting using a stratified split (70% training data and 30% test data), and the application of the Random Forest algorithm for classification. The features used include average academic scores, absences (sick, excused, unexcused), and extracurricular activities. The results showed that the model performed very well, with an accuracy of 1.000 on the test data and an average cross-validation accuracy of 0.9865. Additionally, the precision, recall, and F1-score each reached 1.000. The classification results identified 13 students as high achievers, with the largest distribution coming from 11th grade class 1. These findings indicate that the Random Forest method is capable of producing accurate and consistent classifications and is effective in integrating various assessment indicators. This study is expected to support more objective and comprehensive decision-making within educational evaluation systems and to contribute to the development of more holistic classification models for assessing student success in school, based not only on academic achievement but also on important non-academic aspects.]]>
