Penerapan Penerapan Jaringan Syaraf Tiruan Untuk Pengklasifikasi Mahasiswa Berpotensi Drop Out - Lubis, Dikko Rizky Bintang; Tua, Anri Hafiz; Siregar, Muharram Soleh; Armansyah, Armansyah (JATISI, 2024)
This research aims to classify students who have the potential to drop out using the Multilayer Perceptron (MLP) Backpropagation Artificial Neural Network method. The dataset consists of 1337 students which are then divided into training and test data with a ratio of 80%:20%. The classifier results show an accuracy of 94.7% for training data and 95.9% for test data. These findings indicate that the Backpropagation method with the MLP model is able to provide a very high level of accuracy, on average reaching 95%. This research is important because it can help campuses identify students who have the potential to drop out and provide timely intervention to prevent this. In this way, drop out prevention efforts can be improved, ensuring student academic success.