Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability

Gunadi Widi Nurcahyo; Akbari Wafridh; Yuhandri

doi:10.29207/resti.v8i4.5507

Gunadi Widi Nurcahyo Universitas Putra Indonesia YPTK
Akbari Wafridh Universitas Putra Indonesia YPTK
Yuhandri Universitas Putra Indonesia YPTK

DOI: https://doi.org/10.29207/resti.v8i4.5507

Keywords: data prediction, backpropagation, genetic algorithm, survey data

Abstract

Anomalies and data unavailability are significant challenges in conducting surveys, affecting the validity, reliability, and accuracy of analysis results. Various methods address these issues, including the Backpropagation Neural Network (BPNN) for data prediction. However, BPNN can get stuck in local minima, resulting in suboptimal error values. To enhance BPNN's effectiveness, this study integrates Genetic Algorithm (GA) optimization, forming the BPGA method. GA is effective in finding optimal parameter solutions and improving prediction accuracy. This research uses data from the 2022 National Socio-Economic Survey (Susenas) in Solok District to compare the prediction performance of BPNN, Multiple Imputation (MI), and BPGA methods. The comparison involves training the models with a subset of the data and testing their predictions on a separate subset. The BPGA method demonstrates superior accuracy, with the lowest mean squared error (MSE) and highest average accuracy, outperforming both BPNN and MI methods.

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