Perbandingan Metode Random Forest dengan Decision Tree pada Sistem Rekomendasi Olahraga Berdasarkan Karakteristik Kepribadian

Galih Tri Ardiansyah (Institut Teknologi & Bisnis Asia, Malang)
Titania Dwiandini (Institut Teknologi & Bisnis Asia, Malang)

TIN: TERAPAN INFORMATIKA NUSANTARA
Vol 7 No 2 (2026): July 2026

Selecting sports that match individual characteristics is an important factor in increasing motivation, comfort, and consistency in performing physical activities. Each individual has different psychological characteristics, activity preferences, exercise intensity levels, and desired exercise goals. One approach that can be used to provide more personalized sports recommendations is by utilizing personality characteristics based on the Big Five Personality (OCEAN) model. This study aims to compare the performance of the Decision Tree and Random Forest algorithms in classifying sports categories based on personality characteristics, exercise intensity, social preferences, exercise location, and exercise goals. The novelty of this research lies in the implementation and comparative evaluation of these two algorithms in a Big Five Personality-based sports recommendation system, which has not been widely developed. The dataset used was obtained from sports experts, consisting of 603 records containing personality attributes and supporting factors related to sports activities. The dataset was divided using the train-test split method with a proportion of 67% training data and 33% testing data. The research stages included data validation, categorical attribute transformation using Ordinal Encoding, classification model development, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the Random Forest algorithm achieved better performance than Decision Tree, with an accuracy of 82.91%, precision of 0.89, recall of 0.83, and F1-score of 0.81. Meanwhile, Decision Tree obtained an accuracy of 77.89%, precision of 0.65, recall of 0.78, and F1-score of 0.70. These results indicate that the ensemble approach in Random Forest is capable of capturing more complex data patterns and producing more accurate sports category classifications. This research is expected to serve as a foundation for developing a more adaptive and personalized sports recommendation system based on user characteristics.