Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability
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 loc...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5507 |
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| author | Gunadi Widi Nurcahyo Akbari Wafridh Yuhandri |
| author_facet | Gunadi Widi Nurcahyo Akbari Wafridh Yuhandri |
| author_sort | Gunadi Widi Nurcahyo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fabedd2ec8b44818aef36209b176c1d1 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-fabedd2ec8b44818aef36209b176c1d12025-01-13T03:33:03ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-08-018444745310.29207/resti.v8i4.55075507Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data UnavailabilityGunadi Widi Nurcahyo0Akbari Wafridh1Yuhandri2Universitas Putra Indonesia YPTKUniversitas Putra Indonesia YPTKUniversitas Putra Indonesia YPTK 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5507data predictionbackpropagationgenetic algorithmsurvey data |
| spellingShingle | Gunadi Widi Nurcahyo Akbari Wafridh Yuhandri Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) data prediction backpropagation genetic algorithm survey data |
| title | Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability |
| title_full | Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability |
| title_fullStr | Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability |
| title_full_unstemmed | Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability |
| title_short | Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability |
| title_sort | improved backpropagation using genetic algorithm for prediction of anomalies and data unavailability |
| topic | data prediction backpropagation genetic algorithm survey data |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5507 |
| work_keys_str_mv | AT gunadiwidinurcahyo improvedbackpropagationusinggeneticalgorithmforpredictionofanomaliesanddataunavailability AT akbariwafridh improvedbackpropagationusinggeneticalgorithmforpredictionofanomaliesanddataunavailability AT yuhandri improvedbackpropagationusinggeneticalgorithmforpredictionofanomaliesanddataunavailability |