Implementation of the Random Forest Algorithm with Optuna Optimization in Lung Cancer Classification - Yaqin, Ahmad Ainul; Barata, Mula Agung; Mahmudah, Nur (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2025)
Lung cancer remains one of the leading causes of death worldwide, with many sufferers unaware of their condition until it is too late for treatment. Therefore, high-accuracy prediction methods are urgently needed for early detection of lung cancer. This research uses the Random Forest algorithm, known for its excellent performance in medical data classification. In this study, modeling was optimized by implementing hyperparameter optimization using Optuna. The results of the generated model show an accuracy rate of 98.6%, which is highly significant in the context of early lung cancer detection. Additionally, this algorithm demonstrated 100% recall for the positive class and 97% for the negative class, indicating that the model is highly effective in identifying patients who truly have lung cancer. Another advantage of this model is seen in the AUC (Area Under the Curve) value reaching 1, indicating 100% accurate predictions. With these results, this research affirms the importance of using the Random Forest algorithm in developing early detection systems for lung cancer. This not only can improve treatment success rates but also significantly reduce mortality rates from lung cancer.