Optimasi Fungsi Aktivasi pada Artificial Neural Network untuk Prediksi Gagal Jantung Secara Akurat - Raharjo, Mokhamad Ramdhani; Indra Riyana Rahadjeng; Siregar, Muhammad Noor Hasan; Alkhairi, Putrama (Forum Kerjasama Pendidikan Tinggi, 2025)
Heart failure is one of the major health problems that can be fatal if not diagnosed properly and quickly. Therefore, early prediction using artificial intelligence models, especially Artificial Neural Network (ANN), is needed to improve the accuracy in detecting heart failure. This study aims to optimize the activation function in ANN to predict heart failure accurately. Several optimization algorithms tested, namely Adam, RMSprop, SGD, Adagrad, and Adadelta, were used to evaluate model performance in terms of accuracy, precision, recall, and F1-score. The results showed that the Adam optimization algorithm provided the best performance with an accuracy of 86.74%, precision of 75.12%, recall of 66.67%, and F1-score of 70.64%. Meanwhile, other algorithms such as RMSprop, SGD, Adagrad, and Adadelta showed lower performance, with some metrics reaching 0%. This study shows that proper activation function optimization in ANN is very important to improve the model's ability to predict heart failure with a high level of accuracy.