.png)
Utilizing ChatGPT as a Large Language Model for Qualitative Decision Tree Modeling: A Proof-of-Concept for Strengthening Food Security in Indonesia
Sari
This study explores the use of Artificial Intelligence, specifically ChatGPT as a large language model, in constructing a qualitative decision tree to support food security analysis in Indonesia. Framed within the FAO’s four-pillar approach—availability, access, utilization, and stability—the research adopts a methodological proof-of-concept design and does not rely on primary or secondary empirical datasets. Instead, the analysis is based on AI-generated reasoning derived from structured prompts, which are systematically organized into a conceptual decision tree framework. Validation is conducted through interpretative comparison with established theoretical frameworks and national policy documents, rather than empirical testing or expert elicitation. The resulting model provides a structured representation of strategic pathways and potential policy options, highlighting the advantages of AI-assisted modeling in terms of speed, scalability, and integrative synthesis of knowledge. However, the model remains qualitative and exploratory, with limitations related to contextual specificity, potential bias, and the absence of real-time data. The findings suggest that AI can function as a complementary analytical tool for structuring policy-relevant insights, although its application requires careful validation and should not be interpreted as evidence of policy effectiveness.
Kata Kunci
Teks Lengkap:
PDFReferensi
[FAO] Food and Agriculture Organization. (2020). Addressing the Impacts of COVID-19 in Food crises April–December 2020: FAO’s Component of the Global COVID-19 Humanitarian Response Plan (Vol. 2019, Issue December). https://doi.org/https://doi.org/10.4060/ca8497en
[FAO] Food and Agriculuture Organization. (2021). The State of Food Security and Nutrition in The World 2021. Food and Agriculuture Organization. https://fscluster.org/news/state-food-security-and-nutrition-world-2#:~:text=Around a tenth of the,the previous five years combined.
Achmad, A. L. H., Chaerani, D., & Perdana, T. (2021). Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization. Heliyon, 7(1207), e08448. https://doi.org/10.1016/j.heliyon.2021.e08448
Adamchick, J., & Perez, A. M. (2020). Choosing Awareness Over Fear: Risk Analysis and Free Trade Support Global Food Security. Global Food Security, 26(September), 100445. https://doi.org/10.1016/j.gfs.2020.100445
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/https://doi.org/10.1016/j.strueco.2025.10.006
Allahyari, M. S., Berjan, S., El Bilali, H., Ben Hassen, T., & Marzban, S. (2025). Assessing the use of ChatGPT among agri-food researchers: A global perspective. Journal of Agriculture and Food Research, 19, 101616. https://doi.org/https://doi.org/10.1016/j.jafr.2024.101616
Ansari, A., Pranesti, A., Telaumbanua, M., Alam, T., Taryono, Wulandari, R. A., Nugroho, B. D. A., & Supriyanta. (2023). Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach. Heliyon, 9(9), e19639. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e19639
Arora, N., Bhagat, S., Dhama, R., & Bagler, G. (2025). Machine learning and natural language processing models to predict the extent of food processing. Journal of Food Composition and Analysis, 146, 107938. https://doi.org/https://doi.org/10.1016/j.jfca.2025.107938
Bernaschi, D., Marino, D., Cimini, A., & Mazzocchi, G. (2023). The Social Exclusion Perspective of Food Insecurity : The Case of Blacked-Out Food Areas. Sustainability, 15(2974), 1–18. https://doi.org/https://doi.org/10.3390/su15042974
Brennan, D. C. (2003). Price dynamics in the Bangladesh rice market: implications for public intervention. Agricultural Economics, 29(1), 15–25. https://ageconsearch.umn.edu/record/177986?ln=en
Cao, G., Kornher, L., & Brandi, C. (2025). How robust are machine learning approaches for improving food security amid crises? - Evidence from COVID-19 in Uganda. World Development, 196, 107171. dhttps://doi.org/https://doi.org/10.1016/j.worlddev.2025.107171
Cattaneo, A., Federighi, G., & Vaz, S. (2021). The environmental impact of reducing food loss and waste: A critical assessment. Food Policy, 98, 101890. https://doi.org/https://doi.org/10.1016/j.foodpol.2020.101890
Chaudhry, I., Suleman, R., Bhatti, A., & Ullah, I. (2021). Review: Food price Fluctuations and Its Influence on Global Food Market. Annals of Social Sciences and Perspective, 2(1), 21–33. https://doi.org/10.52700/assap.v2i1.33
Chauhan, C., Dhir, A., Ul, M., & Salo, J. (2021). Food loss and waste in food supply chains . A systematic literature review and framework development approach. Journal of Cleaner Production, 295, 126438. https://doi.org/10.1016/j.jclepro.2021.126438
Erdogan, S., Kartal, M. T., & Pata, U. K. (2024). Does Climate Change Cause an Upsurge in Food Prices? Foods, 13(1), 1–20. https://doi.org/10.3390/foods13010154
Gopinath, M., Mullen, K., & Gulati, A. (2004). Domestic support to agriculture in the European Union and the united states: Policy developments since 1996 (Working or Discussion Paper). https://ageconsearch.umn.edu/record/60452?ln=en
Haq, S. U., Shahbaz, P., Abbas, A., Batool, Z., Alotaibi, B. A., & Tratore, A. (2022). Tackling Food and Nutrition Insecurity among Rural Inhabitants : Role of Household-Level Strategies with a Focus on Value Addition , Diversification and Female Participation. Land, 11(254), 1–19. https://doi.org/https://doi.org/10.3390/land11020254
Ji, G., Zhong, H., Feukam Nzudie, H. L., Wang, P., & Tian, P. (2024). The Structure, Dynamics, and Vulnerability of the Global Food Trade Network. Journal of Cleaner Production, 434, 140439. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.140439
Lavelli, V. (2021). Circular food supply chains – Impact on value addition and safety. Trends in Food Science & Technology, 114, 323–332. https://doi.org/https://doi.org/10.1016/j.tifs.2021.06.008
Ma’Mun, S. R., Loch, A., & Young, M. D. (2021). Sustainable irrigation in Indonesia: A case study of Southeast Sulawesi Province. Land Use Policy, 111, 105707. https://doi.org/https://doi.org/10.1016/j.landusepol.2021.105707
Machefer, M., Thomas, A.-C., Meroni, M., Veiga Lopez Pena, J. M., Ronco, M., Corbane, C., & Rembold, F. (2025). Potential and limitations of machine learning modeling for forecasting Acute Food Insecurity. Global Food Security, 45, 100859. https://doi.org/https://doi.org/10.1016/j.gfs.2025.100859
Malau, L. R. E., Rambe, K. R., Ulya, N. A., & Purba, A. G. (2023). The Impact Of Climate Change On Food Crop Production In Indonesia: Jurnal Penelitian Pertanian Terapan, 23(1), 34–46. https://doi.org/10.25181/jppt.v23i1.2418
Meng, H., & Qian, L. (2024). Performances of Different Yield-Detrending Methods in Assessing the Impacts of Agricultural Drought and Flooding: A Case Study in The Middle-and-Lower Reach of the Yangtze River, China. Agricultural Water Management, 296(July 2023), 108812. https://doi.org/10.1016/j.agwat.2024.108812
Mohamed, J. H., Aweke, C. S., & Muleta, T. T. (2025). Impact of Livelihood Diversification on Rural Households ’ Food and Nutrition Security : Evidence from West Shoa Zone of Oromia Regional. Food and Nutrition Policy, 9(December 2024). https://doi.org/10.1016/j.cdnut.2024.104521
Nurhasan, M., Ariesta, D. L., Utami, M. M. H., Fahim, M., Aprillyana, N., Maulana, A. M., & Ickowitz, A. (2024). Dietary transitions in Indonesia: the case of urban, rural, and forested areas. Food Security, 16(6), 1313–1331. https://doi.org/10.1007/s12571-024-01488-3
Nuryanti, S., Hakim, D. B., Siregar, H., & Sawit, M. H. (2017). Political economic analysis of rice self-sufficiency in Indonesia. Indonesian Journal of Agricultural Science, 18(2), 77–86. https://doi.org/http//dx.doi.org/10.21082/ijas.v.18.n2.2017.p.77–86
Okura, F., Budiasa, I. W., & Kato, T. (2022). Exploring a Balinese irrigation water management system using agent-based modeling and game theory. Agricultural Water Management, 274, 107951. https://doi.org/https://doi.org/10.1016/j.agwat.2022.107951
Pandey, D. K., & Mishra, R. (2024). Towards sustainable agriculture: Harnessing AI for global food security. Artificial Intelligence in Agriculture, 12, 72–84. https://doi.org/https://doi.org/10.1016/j.aiia.2024.04.003
Perdana, T., Chaerani, D., Achmad, A. L. H., & Hermiatin, F. R. (2020). Scenarios for handling the impact of COVID-19 based on food supply network through regional food hubs under uncertainty. Heliyon, 6(10), 1–22. https://doi.org/10.1016/j.heliyon.2020.e05128
Putra, A. W., Supriatna, J., Koestoer, R. H., & Soesilo, T. E. (2021). Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market. Sustainability, 13(8). https://doi.org/10.3390/su13084465
Rashid, S., Jr., R. C., & Gulati, A. (2005). Grain marketing parastatals in Asia: why do they have to change now? (p. 75). International Food Policy Research Institute. https://ageconsearch.umn.edu/record/59830?ln=en
Sapthu, A., Louhenapessy, F. H., Saptenno, F., Louhenapessy, D., Duwila, U., & Jani. (2024). Analysis of the Elasticity of Rice Demand for Poor Households in Sirimau District, Ambon City in 2024. ARRUS Journal of Social Sciences and Humanities, 4(3), 419–428. https://doi.org/10.35877/soshum2684
Shobur, M., Nyoman Marayasa, I., Bastuti, S., Muslim, A. C., Pratama, G. A., & Alfatiyah, R. (2025). Enhancing food security through import volume optimization and supply chain communication models: A case study of East Java’s rice sector. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100462. https://doi.org/https://doi.org/10.1016/j.joitmc.2024.100462
Sisay, K. (2024). Impacts of multiple livelihood diversification strategies on diet quality and welfare of smallholder farmers: Insight from Kaffa zone of Ethiopia. Cleaner and Responsible Consumption, 12, 100161. https://doi.org/https://doi.org/10.1016/j.clrc.2023.100161
Suárez, C. A., Guaño, W. A., Pérez, C. C., & Roa-López, H. (2024). Multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse. Operations Research Perspectives, 12, 100304. https://doi.org/https://doi.org/10.1016/j.orp.2024.100304
Suryana, A. (2014). Menuju Ketahanan Pangan Indonesia Berkelanjutan 2025 : Tantangan dan Penanganannya. Forum Penelitian Agro Ekonomi, 32(2), 123–135. https://doi.org/http://dx.doi.org/10.21082/fae.v32n2.2014.123-135
Ty, T. Van, Minh, H. V. T., Avtar, R., Kumar, P., Hiep, H. Van, & Kurasaki, M. (2021). Spatiotemporal Variations in Groundwater Levels and the Impact on Land Subsidence in CanTho, Vietnam. Groundwater for Sustainable Development, 15, 100680. https://doi.org/https://doi.org/10.1016/j.gsd.2021.100680
Walls, H., Baker, P., Chirwa, E., & Hawkins, B. (2019). Food Security, Food Safety & Healthy Nutrition: Are They Compatible? Global Food Security, 21, 69–71. https://doi.org/10.1016/j.gfs.2019.05.005
Wang, K., Mirosa, M., Hou, Y., & Bremer, P. (2025). Advancing food safety behavior with AI: Innovations and opportunities in the food manufacturing sector. Trends in Food Science & Technology, 161, 105050. https://doi.org/https://doi.org/10.1016/j.tifs.2025.105050
Wright, T., & Meylinah, S. (2017). USDA Foreign Agricultural Service Global Agricultural Information Network (GAIN) Report 13 April 2017. https://gain.fas.usda.gov/Recent GAIN Publications/Sugar Annual_Jakarta_Indonesia_4-13-2017.pdf
Yamaguchi, M., Praditsorn, P., Purnamasari, S. D., Sranacharoenpong, K., Arai, Y., Sundermeir, S., Gittelsohn, J., Hadi, H., & Nishi, N. (2022). Measures of Perceived Neighborhood Food Environments and Dietary Habits: A Systematic Review of Methods and Associations. Nutrients, 14. https://doi.org/10.3390/nu14091788
Zaman, S. I., Khan, S., Zaman, S. A. A., & Khan, S. A. (2023). A grey decision-making trial and evaluation laboratory model for digital warehouse management in supply chain networks. Decision Analytics Journal, 8, 100293. https://doi.org/https://doi.org/10.1016/j.dajour.2023.100293
DOI: https://doi.org/10.61316/jrma.v4i1.229
Refbacks
- Saat ini tidak ada refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International