Autokorelasi Spasial Prevalensi Ketidakcukupan Konsumsi Pangan di Pulau Kalimantan Tahun 2024
DOI:
https://doi.org/10.61220/Keywords:
Food Security, Moran's I, Prevalence of Undernourishment, Spatial AutocorrelationAbstract
Food security remains a serious challenge in Indonesia, marked by high disparities among regencies/ cities, including in Kalimantan Island. This study aims to identify disparities and spatial patterns in the prevalence of food consumption inadequacy in Kalimantan Island in 2024, test for spatial autocorrelation, and identify regional clusters based on prevalence characteristics. The methods used were exploratory and inferential spatial analysis, including the measurement of global spatial autocorrelation using Moran's I and the identification of local clusters using LISA (Local Indicators of Spatial Association). The results reveal significant positive spatial autocorrelation (global Moran's I = 0.64), with “High-High” clusters concentrated in West Kalimantan and North Kalimantan, and “Low-Low” clusters in South Kalimantan. These findings imply the necessity for cluster-based and context-specific policy approaches to effectively enhance food security.
Downloads
References
[1] FAO, The State of Food Security and Nutrition in the World 2025. Rome: Food and Agriculture Organization, 2025.
[2] Badan Pangan Nasional, Rencana Aksi Badan Pangan Nasional Tahun 2025. Jakarta: Badan Pangan Nasional, 2025.
[3] Badan Pusat Statistik, “Prevalensi Ketidakcukupan Konsumsi Pangan (Persen) Per Kabupaten/Kota 2024,” 2025. [Online]. Available: bps.go.id/id/statistics-table/2/MjI2OSMy/prevalensi-ketidakcukupan-konsumsi-pangan--persen--per-kabupaten-kota.html [Accessed: 25-Jul-2025].
[4] Baharuddin, I. Yahya, and M. Ihwal, “Otokorelasi Spasial pada Prevalensi Balita Stunting, Wasting, Underweight, dan Overweight di Pulau Sulawesi Tahun 2022,” Journal of Mathematics, Computation and Statistics, vol. 7, no. 2, pp. 472-482, 2024, doi: https://doi.org/10.35580/jmathcos.v7i2.2408.
[5] A. Nambiar, S. B. Agnihotri, A. Singh, and D. Arunachalam, “Region Matters: Mapping the Contours of Undernourishment among Children in Odisha, India,” PLoS ONE, vol. 17, no. 6, p. e0268600, 2022, doi: 10.1371/journal.pone.0268600.
[6] V. N. Maniragaba, L. K. Atuhaire, and P. C. Rutayisire, “Undernutrition among the Children below Five Years of Age in Uganda: A Spatial Analysis Approach,” BMC Public Health, vol. 23, p. 390, 2023, doi: 10.1186/s12889-023-15214-9.
[7] M. M. Ayalew, Z. G. Dessie, A. A. Mitiku, and T. Zewotir, “Exploring the Spatial and Spatiotemporal Patterns of Severe Food Insecurity across Africa (2015-2021),” Scientific Reports, vol. 14, p. 29846, 2024, doi: 10.1038/s41598-024-78616-8.
[8] Baharuddin, Agusrawati, and L. Laome, “Pemodelan Regresi Spasial pada Tingkat Kemiskinan di Pulau Sulawesi,” ESTIMASI: Journal of Statistics and Its Application, vol. 6, no. 1, pp. 89–100, 2025, doi: 10.20956/ejsa.v6i1.40494.
[9] B. Girma, L. D. Sasahu, and A. Rahman, “Spatial Distribution of Stunting among Breast Feeding Children in Sub-Sahara Africa,” PLoS ONE, vol. 20, no. 6, p. e0325812, 2025, doi: 10.1371/journal.pone.0325812.
[10] R. Amir-ud-Din, S. Fawad, L. Naz, S. Zafar, R. Kumar, and S. Pongpanich, “Nutritional Inequalities among Under-Five Children: A Geospatial Analysis of Hotspots and Cold Spots in 73 Low- and Middle-Income Countries,” International Journal for Equity in Health, vol. 21, p. 135, 2022, doi: 10.1186/s12939 022 01733 1.
[11] F. G. Habtewold and B. G. Arero, “Modeling and Mapping Under-Nutrition among Under-Five Children in Ethiopia: A Bayesian Spatial Analysis,” Frontiers in Public Health, vol. 13, p. 1553908, 2025, doi: 10.3389/fpubh.2025.1553908.





Email : ljmsa@lontaradigitech.com