Penggunaan Feature Space SMOTE Untuk Mengurangi Overfitting Akibat Imbalance Dataset
DOI:
https://doi.org/10.26623/transformatika.v22i2.8305Kata Kunci:
Cnn, imbalance, smote, augmentation, overfittingAbstrak
The creation of a classification model requires careful consideration of several crucial factors to achieve optimal performance. A good model is typically indicated by high accuracy and F1-score values, as well as low loss values. To create a successful model, certain conditions must be met, including selecting the appropriate architecture and ensuring the availability of high-quality data. In this study, a classification model for CT Kidney Stone was developed using an imbalanced dataset obtained from Kaggle. The chosen algorithm for model development was Convolutional Neural Network (CNN), as CNN is known for its effectiveness in image classification tasks. Three different pre-processing approaches were employed in model creation. The first model was built using the imbalanced training data. The second model involved data augmentation, while the third model utilized SMOTE oversampling. Subsequently, all three models were evaluated using private data to assess testing performance and identify any potential overfitting. The research findings revealed that the third model exhibited the best performance among the three, showcasing its superiority in handling the imbalanced dataset and achieving optimal results.Referensi
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