Optimization of Backpropagation (BP) Weight Values Using Particle Swarm Optimization (PSO) to Predict KIP Scholarship Recipients

Abstract

The Indonesia Smart Card (KIP) Lecture program aims to improve the quality of human resources by providing educational assistance to students from underprivileged families. However, the distribution of KIP Lecture in Palembang still faces problems, such as inaccurate targeting and lack of public understanding of this program. The selection process for scholarship recipients is not optimal, causing students who should be prioritized to be overlooked. In addition, decision-making takes a long time due to the many variables that must be considered and the lack of transparency in data processing. This research discusses the Backpropagation (BP) method for predicting KIP College scholarship recipients, which has previously been applied to the classification of educational aid recipients with high accuracies results. However, BP has disadvantages such as minimum local risk and long training time. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights of the BP artificial neural network. PSO is a simple but effective optimization algorithm to find optimal weights more quickly and accurately. The results of previous studies show that the combination of BP with PSO can improve prediction accuracy compared to using BP alone. Therefore, this research aims to develop a more efficient and targeted prediction model for KIP College scholarship recipients through BP optimization using PSO, so that the selection process can be carried out more quickly and accurately.

Keywords

• backpropagation (bp), particle swarm optimization (pso), prediction model, smart indonesia card (kip)