Predictive Modeling of Graduation Outcomes in Islamic Boarding Schools Using Feedforward Neural Networks

Abstract

Pondok Pesantren Nuur Ar Radhiyyah is an Islamic boarding school that places significant emphasis on Quranic memorization, practical religious practices (ibadah amaliyah), and prayer (ibadah sholat) as core educational values for its students. These values play a pivotal role in determining the graduation status of the santri (students) within the institution. This research investigates the application of feedforward neural networks (FFNN) architecture, focusing on the number of hidden layers and neurons per hidden layer, to develop a predictive model for the future graduation status of the santri. The study explores three different activation functions (Sigmoid, ReLU, and Tanh) and assesses their impact on model performance. Our findings reveal that the optimal model for predicting the santri's graduation status involves the use of the Tanh activation function in combination with four hidden layers and four neurons per hidden layer. This model demonstrates outstanding accuracy, precision, and recall rates of 97.6%.