Optimasi Parameter Artificial Neural Network (ANN) Menggunakan Particle Swarm Optimization (PSO) Untuk Pengkategorian Nasabah Bank

Abstract

Artificial Neural Network (ANN), also known as Jaringan Saraf Tiruan, is one of the methods commonly used for pattern recognition, classification, forecasting, and regression, depending on the problem or data used. While the results obtained are generally good, there are often issues with determining the initial parameters as the initial weights, which can lead to non-convergence of results. This is why a method is needed to optimize the ANN parameters to achieve better outcomes. Particle Swarm Optimization (PSO) was chosen as the method to optimize the ANN parameters (PSO-ANN). The best parameter values for PSO were predefined, with w (inertia weight) set to 0.8 and c1 and c2 (acceleration coefficients) set to 1.5. Subsequently, PSO-ANN was trained using a bank customer dataset to determine the categories of customers with credit problems or not. The results were compared with using ANN without parameter optimization. The obtained results showed an Accuracy rate of 82.6%, Precision of 91.1%, and Recall of 37.1%. This represents an improvement compared to the results of ANN without parameter optimization, which had an Accuracy rate of 80.1%, Precision of 89.5%, and Recall of 32.4%.