Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks
This research addresses the persistent global challenge of poverty, with a specific focus on Indonesia, a nation with a population exceeding 270 million. The primary objective is to enhance the precision and reliability of poverty classification using advanced machine learning technologies. We em...
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2024
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my-inti-eprints.20502024-11-26T06:32:58Z http://eprints.intimal.edu.my/2050/ Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks Khalisha, Ariyani Silvia, Ratna M., Muflih Haldi, Budiman Noor, Azijah M.Rezqy, Noor Ridha GF Human ecology. Anthropogeography QA75 Electronic computers. Computer science QA76 Computer software This research addresses the persistent global challenge of poverty, with a specific focus on Indonesia, a nation with a population exceeding 270 million. The primary objective is to enhance the precision and reliability of poverty classification using advanced machine learning technologies. We employed a combination of Bidirectional Gated Recurrent Unit (BiGRU), Backpropagation Neural Network (BPNN), and stacking techniques with AdaBoost to develop an innovative classification model. The methodology involved training each technique separately and then integrating them into a stacked model to leverage their individual strengths. The results were promising, demonstrating a substantial improvement in model performance with precision, recall, and F1 scores reaching as high as 0.98, and an overall accuracy of 98.06%. The use of visual analytics, including pie charts and bar graphs, provided a comprehensive distribution analysis of poverty levels, confirming the balanced nature of the dataset. These findings underscore the critical role of machine learning in formulating effective policies for poverty alleviation and suggest that integrating multiple machine learning algorithm can significantly enhance decision-making processes. The novelty of this research lies in the successful application of a stacked machine learning model combining BiGRU, BPNN, and AdaBoost, which establishes a new benchmark for poverty classification in large-scale social datasets. This study not only contributes to the academic discourse but also paves the way for practical implementations that can drive inclusive and sustainable development. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2050/1/jods2024_51.pdf text en cc_by_4 http://eprints.intimal.edu.my/2050/2/591 Khalisha, Ariyani and Silvia, Ratna and M., Muflih and Haldi, Budiman and Noor, Azijah and M.Rezqy, Noor Ridha (2024) Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks. Journal of Data Science, 2024 (51). pp. 1-6. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
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GF Human ecology. Anthropogeography QA75 Electronic computers. Computer science QA76 Computer software Khalisha, Ariyani Silvia, Ratna M., Muflih Haldi, Budiman Noor, Azijah M.Rezqy, Noor Ridha Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks |
| description |
This research addresses the persistent global challenge of poverty, with a specific focus on
Indonesia, a nation with a population exceeding 270 million. The primary objective is to enhance
the precision and reliability of poverty classification using advanced machine learning
technologies. We employed a combination of Bidirectional Gated Recurrent Unit (BiGRU),
Backpropagation Neural Network (BPNN), and stacking techniques with AdaBoost to develop an
innovative classification model. The methodology involved training each technique separately and
then integrating them into a stacked model to leverage their individual strengths. The results were
promising, demonstrating a substantial improvement in model performance with precision, recall,
and F1 scores reaching as high as 0.98, and an overall accuracy of 98.06%. The use of visual
analytics, including pie charts and bar graphs, provided a comprehensive distribution analysis of
poverty levels, confirming the balanced nature of the dataset. These findings underscore the critical
role of machine learning in formulating effective policies for poverty alleviation and suggest that
integrating multiple machine learning algorithm can significantly enhance decision-making
processes. The novelty of this research lies in the successful application of a stacked machine
learning model combining BiGRU, BPNN, and AdaBoost, which establishes a new benchmark for
poverty classification in large-scale social datasets. This study not only contributes to the academic
discourse but also paves the way for practical implementations that can drive inclusive and
sustainable development. |
| format |
Article |
| author |
Khalisha, Ariyani Silvia, Ratna M., Muflih Haldi, Budiman Noor, Azijah M.Rezqy, Noor Ridha |
| author_facet |
Khalisha, Ariyani Silvia, Ratna M., Muflih Haldi, Budiman Noor, Azijah M.Rezqy, Noor Ridha |
| author_sort |
Khalisha, Ariyani |
| title |
Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking
AdaBoost Frameworks |
| title_short |
Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking
AdaBoost Frameworks |
| title_full |
Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking
AdaBoost Frameworks |
| title_fullStr |
Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking
AdaBoost Frameworks |
| title_full_unstemmed |
Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking
AdaBoost Frameworks |
| title_sort |
poverty classification in indonesia using bigru, bpnn, and stacking
adaboost frameworks |
| publisher |
INTI International University |
| publishDate |
2024 |
| url |
http://eprints.intimal.edu.my/2050/1/jods2024_51.pdf http://eprints.intimal.edu.my/2050/2/591 http://eprints.intimal.edu.my/2050/ http://ipublishing.intimal.edu.my/jods.html |
| _version_ |
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13.223943 |