KLASIFIKASI TINGKAT KESELAMATAN PENYAKIT KANKER PAYUDARA MENGGUNAKAN METODE RANDOM FOREST
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Abstract
This study explores the classification of breast cancer safety levels using three distinct methods: Random Forest, Decision Tree, and K-Nearest Neighbors (KNN). The primary focus is on comparing the experimental outcomes and evaluating the models using metrics evaluation, confusion matrices, train and test accuracies, and cross-validation. Random Forest consistently outperforms Decision Tree and KNN in terms of precision and accuracy due to its robustness in handling data variability through multiple independent decision trees, demonstrating a notable advantage despite small differences in performance. Cross-validation is employed to ensure the models generalize well to unseen data. Moreover, tracking train and test accuracies assists in assessing potential overfitting. The study underscores the importance of adapting the choice of classification method to the dataset's unique characteristics. In conclusion, the findings suggest that Random Forest is the optimal choice for breast cancer safety classification in this specific dataset. However, it is essential to consider dataset context when selecting the most suitable classification method for any given scenario. The evaluation results using cross-validation show that Random Forest is the best indicator with the highest accuracy rate, which is 91.49% compared to Decision Tree and K Nearest Neighbors, which are 85.49% and 90.42%.
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