Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
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This Author published in this journals
All Journal Communication in Biomathematical Sciences Jambura Journal of Biomathematics (JJBM)
Lina Aryati
Program Studi Matematika FMIPA UGM
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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A Mathematical Model of Social Interaction between the Sufferers of Cardiovascular and Type 2 Diabetes Mellitus Jannah, Nur Wahidiyatil; Aryati, Lina; Adi-Kusumo, Fajar
Communication in Biomathematical Sciences Vol. 7 No. 1 (2024)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2024.7.1.5

Abstract

Type 2 diabetes mellitus is a non-communicable medical condition that is most commonly suffered in compare to type 1 diabetes, gestational diabetes, or diabetes that is caused by pathogen or disorders. The other important non-communicable medical condition is cardiovascular disease that occurs due to impaired blood circulation in the heart and blood vessels. The unhealthy lifestyle behaviors that mainly influenced by social interactions play an important role to increase the number of prevalence for those diseases. In this paper, we consider a mathematical model of the social interactions effects to the sufferers of the cardiovascular and type 2 diabetes mellitus diseases. We separate the population to five sub populations, i.e., individuals with normal weight, individuals who have obesity, individuals with cardiovascular disease only, individuals with type 2 diabetes mellitus disease only, and individuals with both cardiovascular and type 2 diabetes mellitus diseases. By using linear analysis and bifurcation theory, we determine the steady state conditions analytically and show some scenarios for the population based on variation of the parameters value numerically.
A Reinforcement Learning Based Decision-Support System for Mitigate Strategies During COVID-19: A Systematic Review Rifanti, Utti Marina; Aryati, Lina; Susyanto, Nanang; Susanto, Hadi
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 1: March 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i1.30513

Abstract

The past threat of the COVID-19 pandemic has challenged policymakers to develop effective decision-support systems. Reinforcement learning (RL), a branch of artificial intelligence, has emerged as a promising approach to designing such systems. This systematic review analyzes 20 selected studies published between 2020 and 2024 that apply RL as a decision-making tool for COVID-19 mitigation, focusing on environment models, algorithms, state representation, action design, reward functions, and challenges. Our findings reveal that Q-learning is the most frequently used algorithm, with most implementations relying on SEIR-based models and real-world COVID-19 epidemiological data. Policy interventions, particularly lockdowns, are commonly modeled as actions, while reward functions are health-oriented, economic, or hybrid, with an increasing trend toward multi-objective designs. Despite these advancements, key limitations persist, including data uncertainty, computational complexity, ethical concerns, and the gap between simulated performance and real-world feasibility. This review further identifies a research opportunity to integrate epidemic model formulations with explicit control inputs into RL frameworks, potentially enhancing learning efficiency and bridging the gap between simulation and practice for future pandemic response systems.
Co-Authors Adi Sutanto Adi-Kusumo, Fajar Hadi Susanto Jannah, Nur Wahidiyatil Ridwan, Muh. Rais Susyanto, Nanang Utti Marina Rifanti