Perbandingan Tingkat Akurasi Algoritma Decision Tree dan Random Forest dalam Klasifikasi pada Dataset Heterogen
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Abstract
Decision Tree and Random Forest are two widely used classification algorithms in data mining. However, most previous studies have evaluated these algorithms using a single dataset, making their findings potentially less generalizable across different data characteristics. This study compares the accuracy of Decision Tree and Random Forest using three datasets with distinct characteristics, namely Iris, which is relatively small and simple; Breast Cancer Wisconsin, which contains a larger number of attributes; and Telco Customer Churn, which has a larger data volume and an imbalanced class distribution. The results show that Random Forest achieves higher accuracy on the Breast Cancer Wisconsin dataset (96.49% compared to 92.40%) and the Telco Customer Churn dataset (79.79% compared to 75.77%), whereas Decision Tree performs better on the Iris dataset (93.33% compared to 91.11%). These findings indicate that the performance of the two algorithms is data-dependent, with the advantage of Random Forest becoming more evident when applied to datasets with greater complexity. The results of this study can serve as a reference for selecting an appropriate classification algorithm based on the characteristics of the dataset.
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References
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