Komparasi Kinerja Decision Tree dan Random Forest untuk Menentukan Kelayakan Kandidat Peserta Lomba Robotik

Abstract

The selection process for robotics competition candidates at PT. Sonnic Robotic still relies on instructors’ subjective assessments and lacks standardized criteria, potentially leading to suboptimal decisions. This study aims to compare the performance of the Decision Tree and Random Forest algorithms in classifying the eligibility of robotics competition candidates. The dataset consists of 99 active elementary school students and nine variables, including attendance percentage, monthly scores over five periods, and creativity, subject comprehension, and independence, assessed using a 1–4 rating scale. The data are normalized using Min-Max Normalization, and total scores are then calculated based on expert judgment weights to determine the Eligible and Not Eligible labels using a threshold of 75. Modeling is conducted using Altair AI Studio with 10-fold cross-validation. The comparative results show that Random Forest achieves an accuracy of 91.89%, weighted precision of 92.79%, weighted recall of 91.92%, and a weighted F1-score of 91.93%. Meanwhile, Decision Tree achieves an accuracy of 90.89%, weighted precision of 91.99%, weighted recall of 90.25%, and a weighted F1-score of 90.91%. These results indicate that Random Forest performs better across all the evaluated metrics, although the difference in accuracy between the two algorithms is relatively small. However, this study is limited by its sample size of only 99 students. Therefore, the superiority of Random Forest cannot yet be considered statistically significant, and the generalizability of the findings needs to be further evaluated using larger and more diverse datasets. The contribution of this study is to provide a comparative evaluation of both algorithms and identify a classification approach with the potential to support a more objective, measurable, and consistent selection process for robotics competition candidates.