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

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

data prediction;backpropagation;genetic algorithm;survey data

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 findingoptimal parameter solutionsand 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 method

Gunadi Widi Nurcahyo1*,Akbari Wafridh2, Yuhandri

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5507/947

Universitas Putra Indonesia “YPTK” Padang, Indonesia

04-08-2024

FAJAR BAGUS W

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