Titin Andriyani Atmojo
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Integrating AHP and TBATS for Infectious Disease Prioritization and Forecasting in East Java Supriyanto, Budi Fajar; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7151

Abstract

Agrarian regions like East Java province face complex public health challenges. Some cases are caused by the interaction between social factors, and others by agribusiness factors. An integrative approach is needed to understand the dynamics of disease cases. This study aims to analyse the disease with the highest number of cases and project case trends in East Java using an integrated quantitative approach. Using methods such as the Analytic Hierarchy Process (AHP) to determine disease weights, the TBATS model is used to project case trends through 2028. Standardised multiple regression models were used to assess the influence of social factors (population density, poverty) and agribusiness (rice harvest area, agricultural labour). The data used are secondary time-series data from 2013 to 2023 obtained from BPS, the Health Department, and BMKG. The AHP results show diarrhoea as the disease with the highest weight (0.494), followed by pneumonia (0.112), tuberculosis (0.090), malaria (0.051), and dengue fever (0.049). The TBATS projection indicates medium-term fluctuations with the potential for an increase in dengue fever cases. Meanwhile, the regression results show that people in the agricultural sector are at increased risk of malaria (p = 0.037), while other variables have an influence but are not significant. Therefore, integrating health, social, and agribusiness data is an urgent need. And it can be used for early disease warning systems and more precise public health policy strengthening.
GeoAI for Precision Public Health in Agrarian Economies: Multi-Disease Risk Profiling in Rice Belt in East Java Budi Fajar Supriyanto; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6646

Abstract

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.