Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks

• Main Authors: Khalisha, Ariyani, Silvia, Ratna, M., Muflih, Haldi, Budiman, Noor, Azijah, M.Rezqy, Noor Ridha
• Format: Article
• Language: English; English
• Published: INTI International University 2024
• Subjects: GF Human ecology. Anthropogeography; QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: 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

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.