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Quantifying the Geopark Contribution to the Village Development Index Using Machine Learning—A Deep Learning Approach: A Case Study in Gunung Sewu UNESCO Global Geopark, Indonesia

Author

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  • Rizki Praba Nugraha

    (Study Program of Regional and Rural Development Planning, Graduate School, IPB University, 1, Bogor 16680, Indonesia)

  • Akhmad Fauzi

    (Department of Resources and Environmental Economics, Faculty of Economics and Management, IPB University, 2, Bogor 16680, Indonesia)

  • Ernan Rustiadi

    (Division of Regional Development Planning, Department of Soil Science and Land Resources, Faculty of Agriculture, IPB University, 3, Bogor 16680, Indonesia)

  • Sambas Basuni

    (Department of Forest Resource Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, 4, Bogor 16680, Indonesia)

Abstract

The Gunung Sewu UNESCO Global Geopark (GSUGGp) is one of Indonesia’s 12 UNESCO-designated geoparks. Its presence is expected to enhance rural development by boosting the local economy through tourism. However, there is a lack of statistical evidence quantifying the economic benefits of geopark development, mainly due to the complex, non-linear nature of these impacts and limited village-level economic data available in Indonesia. To address this gap, this study aims to measure how socio-economic and environmental factors contribute to the Village Development Index (VDI) within the GSUGGp area, which includes the districts of Gunung Kidul, Wonogiri, and Pacitan. A machine learning–deep learning approach was employed, utilizing four algorithms grouped into eight models, with hyperparameter tuning and cross-validation, tested on a sample of 92 villages. The analysis revealed insights into how 17 independent variables influence the VDI. The Artificial Neural Network (ANN) algorithm outperformed others, achieving an R-squared of 0.76 and an RMSE of 0.040, surpassing random forest, CART, SVM, and linear models. Economically related factors—considered the foundation of rural development—had the strongest impact on village progress within GSUGGp. Additionally, features related to tourism, especially beach tourism linked to geological landscapes, contributed significantly. These findings are valuable for guiding geopark management and policy decisions, emphasizing the importance of integrated strategies and strong cooperation among local governments at the regency and provincial levels.

Suggested Citation

  • Rizki Praba Nugraha & Akhmad Fauzi & Ernan Rustiadi & Sambas Basuni, 2025. "Quantifying the Geopark Contribution to the Village Development Index Using Machine Learning—A Deep Learning Approach: A Case Study in Gunung Sewu UNESCO Global Geopark, Indonesia," Sustainability, MDPI, vol. 17(15), pages 1-31, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6707-:d:1708157
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