Geo AI-Based Food Policy Navigation in South Sumatra
DOI:
https://doi.org/10.37680/ijief.v6i2.10504Keywords:
food price stability; food security; spatial spillover; remote sensing; Geo-AIAbstract
Food price instability in South Sumatra is structural and recurring, exceeding the national inflation rate. This study develops a Geo AI-based food policy navigation system integrating satellite biophysical indicators, macroeconomic data, and machine learning to support the Regional Inflation Control Team (TPID). The data comprise a monthly panel from 11 of 17 districts (March 2021 to December 2025), selected based on available retail price data. Methods include GSTAR, SAR-X, SARIMAX, and XGBoost for forecasting; SHAP, Granger causality, and VAR decomposition for root cause diagnostics; PCA for the Composite Food Security Index (IKP Sumsel); Monte Carlo simulation for shock scenarios; and MCDA-SAW for policy prioritization. GSTAR achieves the highest accuracy (MAPE 2.56% for rice, 3.36% for eggs, 4.34% for chicken). Root causes vary across commodities, with 54.55% of districts under Alert status by December 2025 and karhutla as the most significant threat. The system integrates real-time satellite signals, including NDVI, rainfall, and forest fire hotspots, directly into TPID decision-making to enable anticipatory rather than reactive interventions. The study recommends differentiated 4K Strategy priorities for Musi Banyuasin, Ogan Komering Ilir, and Musi Rawas, and fire mitigation integrated into peatland food security policy.
References
Abdi, A. H., Mohamed, A. A., & Sheikh, S. N. (2025). Navigating paths to food security in East Africa: strengthening rural development amid climate shocks, political instability, and rising food prices. Frontiers in Political Science, 7. https://doi.org/10.3389/fpos.2025.1636407
Algieri, B., Kornher, L., & von Braun, J. (2025). The changing drivers of inflation – the case of food: Macroeconomics, speculation, climate change and war. Structural Change and Economic Dynamics, 75, 782–800. https://doi.org/10.1016/j.strueco.2025.10.006
Anselin, L. (1988). Spatial econometrics: Methods and models (Vol. 4). Springer Netherlands. https://doi.org/10.1007/978-94-015-7799-1
Bank Indonesia. (2025). Laporan perekonomian Provinsi Sumatera Selatan triwulan IV 2025. https://www.bi.go.id/id/publikasi/laporan/lpp/
Bank Indonesia. (2026). Laporan perekonomian Provinsi Sumatera Selatan Februari 2026. https://www.bi.go.id/id/publikasi/laporan/lpp/Pages/Laporan-Perekonomian-Provinsi-Sumatera-Selatan-Februari-2026.aspx
Badan Informasi Geospasial. (n.d.). Portal geospasial. Kementerian Agraria dan Tata Ruang/Badan Pertanahan Nasional. https://tanahair.indonesia.go.id/portal-web/
Bapanas. (2024). Peraturan Badan Pangan Nasional Nomor 6 Tahun 2024 tentang perubahan atas Peraturan Badan Pangan Nasional Nomor 5 Tahun 2022 tentang harga acuan pembelian di tingkat produsen dan harga acuan penjualan di tingkat konsumen untuk komoditas jagung, telur ayam ras, dan daging ayam ras. https://peraturan.go.id/id/peraturan-bapanas-no-6-tahun-2024
Chen, W., & Zhao, Y. (2023). Price dynamics and regional integration in China’s rice markets: Evidence from spatial econometric analysis. Foods, 12(17), 3298. https://doi.org/10.3390/foods12173298
Dana, B. S., & Musaiyaroh, A. (2024). Threshold effect of monetary and fiscal policy on inflation in Indonesia. Jurnal Ekonomi Pembangunan, 22(2), 157–170. https://doi.org/10.29259/jep.v22i2.23131
Diawati, L., & Rasyid, A. S. (2025). Mitigating retail rice price volatility for sustainable supply chains: An optimization and regression-based approach. F1000Research, 14, 311. https://doi.org/10.12688/f1000research.161723.2
Dorosh, P., Minot, N., & Rashid, S. (2025). Food price stabilization: Theory and lessons from experience. Food Policy, 137, 102945. https://doi.org/10.1016/j.foodpol.2025.102945
FAO. (2006). Food security (Policy Brief No. 2). Food and Agriculture Organization of the United Nations (FAO). https://www.fao.org/fileadmin/templates/faoitaly/documents/pdf/pdf_Food_Security_Cocept_Note.pdf
Guo, F. (2025). Enhancing food security and trade resilience in sustainable agricultural systems. Frontiers in Sustainable Food Systems, 9. https://doi.org/10.3389/fsufs.2025.1690080
Hossain, S., & Kashem, S. Bin. (2025). Transportation resilience and food security: Developing a conceptual framework through literature review. Frontiers in Sustainable Food Systems, 9. https://doi.org/10.3389/fsufs.2025.1569474
Ikrom, M., Hadi, A. F., Qori’ah, C. G., & Nasir, M. A. (2026). Machine learning approach to predictive modeling of Indonesia’s macroeconomy: Evaluating the predictive power of exogenous variables for forecasting Indonesia’s inflation rate in 2024. AIP Conference Proceedings, 3411(1). https://doi.org/10.1063/5.0323006
Krukovets, D. (2024). Exploring an LSTM-SARIMA routine for core inflation forecasting. Technology Audit and Production Reserves, 3(77), 40–45. https://doi.org/10.2139/ssrn.4832821
Levin, N., & Zhang, Q. (2017). A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas. Remote Sensing of Environment, 190, 366–382. https://doi.org/10.1016/J.RSE.2017.01.006
Li, Z., Yu, H., Song, C., Qi, X., & Zhang, Q. (2026). Lighting up the Arctic: Nighttime light data reveals economic growth in the north in Norway, Sweden and Finland. Polar Science, 48, 101357. https://doi.org/10.1016/j.polar.2026.101357
Maimunif, A., & Madyatmadja, E. D. (2026). An Interpretable Machine Learning Approach for Inflation Forecasting in Indonesia Using Domestic Macroeconomic Indicators. International Journal of Advanced Computer Science and Applications, 17(5), 245–256. https://doi.org/10.14569/IJACSA.2026.0170524
Maruejols, L., Wehner, J., Hoeschle, L., & Yu, X. (2026). Gravity with Lasso: Global conflict with factors of conflict, COVID-19, currency, China, climate change and income ('6C ’). World Economy, 49(2), 346–372. https://doi.org/10.1111/twec.70033
Mellander, C., Lobo, J., Stolarick, K., & Matheson, Z. (2015). Night-time light data: A good proxy measure for economic activity? PLoS ONE, 10(10). https://doi.org/10.1371/journal.pone.0139779
Nuno-Ledesma, J. G., & von Massow, M. (2023). Canadian food inflation: International dynamics and local agency. Canadian Journal of Agricultural Economics, 71(3–4), 393–406. https://doi.org/10.1111/cjag.12341
Pakravan-Charvadeh, M. R., Khan, H. A., & Flora, C. (2020). Spatial analysis of food security in Iran: Associated factors and governmental support policies. Journal of Public Health Policy, 41, 351–374. https://doi.org/10.1057/s41271-020-00221-6
Poudel, D., & Gopinath, M. (2023). Regional heterogeneity in undernourishment: The case of Nepal. Journal of Agribusiness in Developing and Emerging Economies, 15(4), 760–775. https://doi.org/10.1108/JADEE-08-2023-0223
Ratnasari, D. S., & Kusumawardani, P. (2015). Spatial modelling for food vulnerability using remote sensing data and GIS (Case study in Klungkung Regency, Bali). Procedia Environmental Sciences, 24, 15–24. https://doi.org/10.1016/j.proenv.2015.03.003
Shafi, U., Mumtaz, R., Anwar, Z., Ajmal, M. M., Khan, M. A., Mahmood, Z., Qamar, M., & Jhanzab, H. M. (2023). Tackling food insecurity using remote sensing and machine learning-based crop yield prediction. IEEE Access, 11, 108640–108657. https://doi.org/10.1109/ACCESS.2023.3321020
Slimani, H., & Chourabi, C. (2026). Modeling inflation with machine learning: A cross-horizon systematic review—International Journal of Data Science and Analytics.
Waiswa, D., & Yavuz, F. (2023). Market integration and asymmetric price transmission in selected domestic markets for major staple foods in Uganda. Future Business Journal, 9(1), 97. https://doi.org/10.1186/s43093-023-00281-6
Wiggins, S., Ahmed, B. Y., Akullo, B., Barry, B., Dudu, J., Eronmhonsele, J., Kiwala, Y., Ogisi, D., Onokerhoraye, A., Opio, J., Patel, N., & Sulieman, H. (2026). Food prices and food crises since 2020: Evidence from Mali, northeast Nigeria, Sudan, and northern Uganda. Disasters, 50(1). https://doi.org/10.1111/disa.70037
Xing, J., & Sieber, R. (2023). The challenges of integrating explainable artificial intelligence into GeoAI. Transactions in GIS, 27(3), 626–645. https://doi.org/10.1111/tgis.13045
Yuliati, L., Dana, B. S., & Musaiyaroh, A. (2025). Monetary and fiscal policy on inflation in Indonesia: Threshold vector autoregression approach. Media Ekonomi Dan Manajemen, 40(1), 1–11.
Zhang, R., Arnav, A., Karakoc, D. B., & Konar, M. (2026). Compound shocks to agri-food supply chain transport and trade in the United States. Frontiers in Sustainable Food Systems, 9. https://doi.org/10.3389/fsufs.2025.1661492
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dian Rizky, Badara Shofi Dana, Dani Shofi Nurizza

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright:
An author who publishes in Indonesian Journal of Islamic Ekonomics and Finance agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgment of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
License:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial — You may not use the material for commercial purposes.
-
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

.png)



