| dc.description.abstract | detecting them is a major challenge in modern astronomy. One of the primary
methods for detecting exoplanets is the transit method, which takes advantage of the
changes in a star's light flux as a planet passes in front of it. However, the dataset
obtained from NASA's Campaign 3 mission shows significant class imbalance, where
the number of stars without exoplanets far exceeds those with exoplanets. In this
study, a Convolutional Neural Network (CNN) method is used to detect exoplanets
from the imbalanced stellar flux dataset. To address this imbalance, the Synthetic
Minority Over-sampling Technique (SMOTE) is applied, aiming to enhance the
representation of the minority class—exoplanets—without altering the proportion of
the majority class. This research aims to explore how SMOTE can resolve data
imbalance issues and assess its impact on CNN model accuracy in detecting
exoplanets. The model is developed and tested with and without the application of
SMOTE, and the performance results are compared. In the model without SMOTE,
the training accuracy reached 93.5%, and the testing accuracy was 99.12%, though
the model exhibited bias toward the majority class. With SMOTE applied, the
training accuracy reached 99.2%, and the testing accuracy was 98.6%. Although the
overall accuracy slightly decreased, the SMOTE-applied model showed improvement
in detecting exoplanets, as evidenced by better precision, recall, and F1-score
metrics. The application of SMOTE successfully addressed data imbalance and
improved the CNN model's performance in detecting exoplanets, particularly in
identifying the minority class. This study contributes to the development of machine
learning-based exoplanet detection methods, which can serve as a reference for
future research in the exploration of planets beyond the solar system. | en_US |
