Klasifikasi Efisiensi Pembakaran Kalori Menggunakan Algoritma Decision Tree pada Data Aktivitas Fisik
Keywords:
Decision Tree, Classification, Machine Learning, Calorie BurningAbstract
This study aims to classify calorie-burning efficiency based on physical activity data using the Decision Tree algorithm. The dataset includes various activity-related variables such as duration, intensity, and type of activity that influence the number of calories burned. The classification is divided into three main categories: Low Efficiency, Moderate, and High Efficiency. The Decision Tree model was developed and evaluated using performance metrics including accuracy, precision, recall, and F1-score. The experimental results show that the model achieved an accuracy of 87.94%, with a precision of 89.43%, recall of 87.94%, and an F1-score of 88.66%. Although the overall performance of the model is considered good, further analysis reveals the presence of class imbalance, where the Low Efficiency category dominates the dataset. This imbalance leads to relatively poor performance in minority classes such as High Efficiency and Moderate, as indicated by low precision and recall values for these categories. Therefore, this study demonstrates that the Decision Tree algorithm is effective for classifying calorie-burning efficiency. However, further improvements are needed to address class imbalance in order to enhance classification performance across all categories
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