GeoAI for Precision Public Health in Agrarian Economies: Multi-Disease Risk Profiling in Rice Belt in East Java
DOI:
https://doi.org/10.25047/ijhitech.v3i2.6646Keywords:
GeoAI, clustering, agribusiness epidemiology, infectious diseases, spatial analysis, east javaAbstract
Public health and food security, particularly in the agribusiness sector, are interconnected. As one of the largest rice-producing provinces in Indonesia, East Java faces numerous infectious diseases. To develop a spatial typology of health-agribusiness risks, this study combines epidemiology, agribusiness, and computer science with a Geospatial Artificial Intelligence (GeoAI) approach.The data includes cases of ten infectious diseases (2015–2024), rice harvested area, number of farmers, and district/city population in East Java. Cases were normalized per 100,000 population, and agribusiness indicators were converted to harvested area per farmer ratios. The analysis used internal validation (silhouette score, Davies–Bouldin Index), K-Means clustering, and spatial validation (Moran's I). Results are displayed on OpenStreetMap.Agribusiness can be divided into three main typologies: (1) strong agribusiness with moderate risk; (2) multisector agribusiness with high risk and moderate agribusiness; and (3) moderate agribusiness with a prevalence of lung disease and diarrhea. Moran's I = -0.0263 (p=0.5678), indicating that spatial distribution is not significant. The results suggest that public health does not always correlate with food production intensity. By integrating epidemiology, agribusiness, and GeoAI to support appropriate public health in agricultural areas, this study adds to the international literature.
References
[1] Badan Pusat Statistik, “Jawa Timur kini jadi lumbung padi terbesar Indonesia tahun ini,” contan.co.id.
[2] D. Amalia et al., “Pola Kecenderungan Penyakit Menular Terhadap Topografi Kabupaten/Kota di Jawa Timur Menggunakan Analisis Korespondensi,” Jurnal Sains Matematika dan Statistika, vol. 9, no. 1, 2023, doi: 10.24014/jsms.v9i1.20853.
[3] N. Sintia and L. Y. Hendrati, “Hubungan Kasus Demam Berdarah Dengue dengan Kepadatan Penduduk di Jawa TimurTahun 2019-2020,” Media Gizi Kesmas, vol. 12, no. 2, 2023, doi: 10.20473/mgk.v12i2.2023.583-588.
[4] C. Dewanti, V. Ratnasari, and A. T. Rumiati, “Pemodelan Faktor-Faktor yang Memengaruhi Status Balita Stunting di Provinsi Jawa Timur Menggunakan Regresi Probit Biner,” Jurnal Sains dan Seni ITS, vol. 8, no. 2, 2020, doi: 10.12962/j23373520.v8i2.48519.
[5] A. A. Sahara and S. Kamso, “Analisis Spasial Pandemi COVID-19 di Jawa Timur (Januari – Juli Tahun 2021),” Jurnal Kedokteran dan Kesehatan, vol. 18, no. 2, 2022.
[6] F. Umarsyah, N. Bagaskara, and Y. Jumaryadi, “Sistem Pendukung Keputusan Diagnosa Gastroenteritis Menggunakan Metode Analytical Hierarchy Process,” JOINS (Journal of Information System), vol. 6, no. 2, 2021, doi: 10.33633/joins.v6i2.5309.
[7] K. C. Ágoston and M. E.-Nagy, “Mixed integer linear programming formulation for K-means clustering problem,” Cent Eur J Oper Res, vol. 32, no. 1, 2024, doi: 10.1007/s10100-023-00881-1.
[8] N. Imani, T. Shafira, L. Kurnia, E. Rusnita, and ..., “ANALISIS PERBANDINGAN METODE SINGLE LINGKAGE DAN KMEANS CLUSTERING (STUDI KASUS: KASUS DEMAM BERDARAH DENGUE (DBD) DI …,” 2018.
[9] Y. Song, M. Kalacska, M. Gašparović, J. Yao, and N. Najibi, “Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping,” 2023. doi: 10.1016/j.jag.2023.103300.
[10] Y. Ma et al., “Forecasting vegetation dynamics in an open ecosystem by integrating deep learning and environmental variables,” International Journal of Applied Earth Observation and Geoinformation, vol. 114, 2022, doi: 10.1016/j.jag.2022.103060.
[11] H. S. Abdel Magid, M. R. Desjardins, and Y. Hu, “Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life course,” Health Place, vol. 89, p. 103323, Sep. 2024, doi: 10.1016/j.healthplace.2024.103323.
[12] B. Resch, P. Kolokoussis, D. Hanny, M. A. Brovelli, and M. N. Kamel Boulos, “The generative revolution: AI foundation models in geospatial health—applications, challenges and future research,” Int J Health Geogr, vol. 24, no. 1, p. 6, Apr. 2025, doi: 10.1186/s12942-025-00391-0.
[13] F. B. Osei, “Evolution of spatial disease clusters via a Bayesian space-time variability modelling,” Spat Spatiotemporal Epidemiol, vol. 47, p. 100617, Nov. 2023, doi: 10.1016/j.sste.2023.100617.
[14] N. Zhang et al., “Epidemiological characteristics and temporal-spatial clustering analysis on human brucellosis in Jiangsu Province, 2006–2021,” Sci Rep, vol. 13, no. 1, p. 20024, Nov. 2023, doi: 10.1038/s41598-023-46690-z.
[15] K. Takahashi and H. Shimadzu, “Detecting multiple spatial disease clusters: information criterion and scan statistic approach,” Int J Health Geogr, vol. 19, no. 1, p. 33, Dec. 2020, doi: 10.1186/s12942-020-00228-y.
[16] A. Levin-Rector, M. Kulldorff, E. R. Peterson, S. Hostovich, and S. K. Greene, “Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks,” JMIR Public Health Surveill, vol. 10, p. e50653, Jun. 2024, doi: 10.2196/50653.
[17] H. S. Iyer, S. Karasaki, L. Yi, Y. Hswen, P. James, and T. VoPham, “Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review,” Curr Environ Health Rep, vol. 12, no. 1, p. 34, Sep. 2025, doi: 10.1007/s40572-025-00497-4.
[18] G. Mai et al., “Towards the next generation of Geospatial Artificial Intelligence,” International Journal of Applied Earth Observation and Geoinformation, vol. 136, p. 104368, Feb. 2025, doi: 10.1016/j.jag.2025.104368.
[19] J. Qiu, X. Li, H. Zhu, and F. Xiao, “Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease,” Animals, vol. 14, no. 19, p. 2814, Sep. 2024, doi: 10.3390/ani14192814.
[20] H. Zhao, “Design and Implementation of an Improved K-Means Clustering Algorithm,” Mobile Information Systems, vol. 2022, 2022, doi: 10.1155/2022/6041484.
[21] T. M. Massie, G. Weithoff, N. Kuckländer, U. Gaedke, and B. Blasius, “Enhanced Moran effect by Spatial variation in environmental autocorrelation,” Nat Commun, vol. 6, 2015, doi: 10.1038/ncomms6993.
[22] V. F. Rochmah and V. Ratnasari, “Pemodelan Ketahanan Pangan di Jawa Timur Menggunakan Metode Geographically Weighted Ordinal Logistic Regression (GWOLR),” Jurnal Sains dan Seni ITS, vol. 8, no. 2, 2020, doi: 10.12962/j23373520.v8i2.47021.
[23] V. M. K. Kedang, I. Permatasari, T. Chanchaidechachai, and C. Inchaisri, “Spatial-temporal distribution and risk factors of foot and mouth disease outbreaks in Java Island, Indonesia from 2022 to 2023,” BMC Vet Res, vol. 21, no. 1, p. 180, Mar. 2025, doi: 10.1186/s12917-025-04621-2.
[24] Y. Kang et al., “STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity,” International Journal of Geographical Information Science, vol. 36, no. 8, pp. 1518–1549, Aug. 2022, doi: 10.1080/13658816.2022.2053980.
[25] D. Jatim, “Profil Kesehatan Jawa Timur 2022,” Sustainability (Switzerland), vol. 11, no. 1, 2022.
[26] Badan Pusat Statistik Provinsi Jawa Timur, “Jumlah Kasus Penyakit Angka Penemuan TBC, Keberhasilan Pengobatan TBC, Kasus Baru AIDS Menurut Kabupaten/Kota di Provinsi Jawa Timur, 2022,” https://jatim.bps.go.id.
[27] O. P. Barus, C. Nathasya, and J. J. Pangaribuan, “The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering,” Mathematical Modelling of Engineering Problems, vol. 10, no. 1, 2023, doi: 10.18280/MMEP.100135.
[28] E. Muningsih, I. Maryani, and V. R. Handayani, “Penerapan Metode K-Means dan Optimasi Jumlah Cluster dengan Index Davies Bouldin untuk Clustering Propinsi Berdasarkan Potensi Desa,” Jurnal Sains dan Manajemen, vol. 9, no. 1, 2021.
[29] E. Habinuddin, “IDENTIFIKASI AUTOKORELASI SPASIAL PADA PENYEBARAN PENYAKIT DEMAM BERDARAH DENGUE DI KOTA BANDUNG,” Sigma-Mu, vol. 13, no. 1, 2021, doi: 10.35313/sigmamu.v13i1.3648.
[30] S. Novianti, T. Nurkholifa, M. Suryana, and E. Susanto, “Penggunaan Geographical Information System (GIS) untuk Visualisasi Analisis Perilaku Spasial Wisatawan,” Journal of Indonesian Tourism, Hospitality and Recreation, vol. 4, no. 2, 2021, doi: 10.17509/jithor.v4i2.37168.
[31] Muhammad Sajidin, Inggar Saputra, and Wida Nofiasari, “STRATEGI KEAMANAN MARITIM INDONESIA DALAM MENGHADAPI ANCAMAN KEJAHATAN TRANSNASIONAL DI ASIA TENGGARA,” Jurnal Lemhannas RI, vol. 11, no. 3, 2023, doi: 10.55960/jlri.v11i3.476.
[32] H. S. Abdel Magid, M. R. Desjardins, and Y. Hu, “Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life course,” Health Place, vol. 89, p. 103323, Sep. 2024, doi: 10.1016/j.healthplace.2024.103323.
[33] A. M. Ikotun, F. Habyarimana, and A. E. Ezugwu, “Benchmarking validity indices for evolutionary K-means clustering performance,” Sci Rep, vol. 15, no. 1, p. 21842, Jul. 2025, doi: 10.1038/s41598-025-08473-6.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Budi Fajar Supriyanto, Salihati Hanifa, Nesa Ayu Murthisari Putri, Titin Andriyani Atmojo, Waridad Umais Al Ayyubi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
