Sudirman Rizki Ariyanto, Bambang Suprianto, Warju, Ratna Suhartini, Muchlas Samani, Kusuma Refa Haratama
This study optimizes the parameters of an Artificial Neural Network (ANN) to predict the academic performance of Automotive Vocational Education (AVE) students using a hybrid approach of Response Surface Methodology (RSM) and Genetic Algorithm (GA). Although ANNs have potential for educational data mining, parameter determination by trial and error reduces prediction accuracy. This study addresses this problem by applying the Central Composite Design (CCD) to identify influential parameters and their interactions, then refining them using RSM-GA. The subject of this research is Vocational High School (VHS) Antartika 1 Sidoarjo, East Java, Indonesia. The data include 884 students' academic records across 11 subjects from 2021 to 2023. The performance of the ANN is evaluated using the Mean Squared Error (MSE) as the primary criterion. Additionally, this study incorporates the Signal-to-Noise (S/N) Ratio from the Taguchi Method to assess model robustness. The S/N Ratio serves as an indicator of the ANN's stability in error minimization, applying the Smaller-the-Better (STB) criterion to optimize predictive accuracy. The results show the optimal ANN configuration: 3 hidden layers, 20 neurons, tansig-purelin activation function, trainbr algorithm, learning rate 0.001, and 300 epochs, with a Signal-to-Noise Ratio of 22.84 and a correlation coefficient of 0.89. The RSM-GA approach showed a 2.5% increase in the S/N Ratio compared to the CCD experimental method, making it more effective in identifying at-risk students early. These findings provide a systematic framework for ANN optimization in vocational education, with implications for the design of personalized learning interventions. © 2026, Politeknik Negeri Padang. All rights reserved.
Universitas Negeri Surabaya, Ketintang Street, Surabaya, Indonesia
