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Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango 96119, Gorontalo, Indonesia
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Jambura Journal of Biomathematics (JJBM)
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27230317     DOI : https://doi.org/10.34312/jjbm.v1i1
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Jambura Journal of Biomathematics (JJBM) aims to become the leading journal in Southeast Asia in presenting original research articles and review papers about a mathematical approach to explain biological phenomena. JJBM will accept high-quality article utilizing mathematical analysis to gain biological understanding in the fields of, but not restricted to Ecology Oncology Neurobiology Cell biology Biostatistics Bioinformatics Bio-engineering Infectious diseases Renewable biological resource Genetics and population genetics
Arjuna Subject : Matematika - Matematika Terapan
Articles 99 Documents
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Implementation of K-Prototypes with Feature Selection in Clustering Cervical Cancer Patients based on Risk Factors Hati, Wanda Puspita; Sarwinda, Devvi; Handari, Bevina Desjwiandra
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.30552

Abstract

Cancer is a leading cause of death worldwide, resulting in nearly 10 million deaths or almost one-sixth of all deaths in 2020. Effective primary prevention measures can prevent at least 40% of cancer cases. Cancer mortality rates are higher in developing countries than in developed countries, reflecting disparities in addressing risk factors, detection success, and available treatments. Women in developing countries most frequently suffer from cervical cancer. It is crucial for communities, especially women, to have knowledge about the risk factors for cervical cancer. One potential solution to this issue is the role of machine learning in analyzing cervical cancer patient data. This study uses the K-Prototypes clustering algorithm, which can cluster mixed data, both numerical and categorical. Cervical cancer risk factor data were used in this research. Feature selection was performed to improve the performance of the K-Prototypes algorithm, using feature selection methods Variance Threshold and Correlation Coefficient. The best performance of the K-Prototypes algorithm was obtained using the Correlation Coefficient, as reviewed based on a Silhouette Coefficient of 0.6, a Davies-Bouldin Index of 0.6, and a Calinski-Harabasz Index of 1.080. Interpretation of the clusters formed revealed major differences in the characteristics of risk factors between two clusters, namely age, menopause, and health conditions such as leukorrhea, bleeding, lower abdominal pain, and loss of appetite. Meanwhile, factors related to previous history, reproductive health, and nutritional issues did not show significant differences. The K-Prototypes algorithm is expected to be a solution in identifying groups based on cervical cancer risk factors to assist medical professionals in decision-making and subsequent actions, as well as to provide knowledge to the public.
Epidemic Dynamics with Nonlinear Incidence Considering Vaccination Effectiveness Kamalia, Putri Zahra; Aldila, Dipo
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.33815

Abstract

This paper presents a mathematical model that examines the effect of nonlinear incidence on disease transmission dynamics.  Furthermore, we also accommodate newborn and adult vaccination strategy as the prevention strategy to prevent rapid spread of the disease due to nonlinear incidence rate. Assuming a constant population  size,  the  system is  reduced  to  a  two-dimensions and  nondimensionalized using  the  average infectious period as the time scale.   Analytical results reveal the existence of both disease-free and endemic equilibria, with the possibility of backward bifurcation when the nonlinear incidence parameter exceeds a critical threshold.   This implies that disease persistence may still occur even when the basic reproduction number is less than one.  Numerical simulations using MATCONT conducted to visualize the occurrence of both forward and backward bifurcations phenomena.    Using COVID-19 parameter values,  a  global sensitivity analysis via Partial Rank Correlation Coefficient - Latin Hypercube Sampling method indicates that newborn vaccination has a stronger impact on reducing the basic reproduction number. These findings provide important insights for designing effective vaccination strategies and understanding the complex dynamics arising from nonlinear transmission and imperfect immunization.
Stem Cell Based Fractional-Order Dynamical Model of Psoriasis: A Mathematical Study Kushary, Subhankar; Ghosh, Tushar; Makinde, Oluwole Daniel; Li, Xue-Zhi; Roy, Priti Kumar
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.33134

Abstract

Psoriasis is a chronic  autoimmune skin  disorder driven by  dysregulated immune responses,  where  abnormal interactions between  T  cells  and  dendritic cells  lead to  excessive   inflammatory  cytokine  production.    This triggers the hyper-proliferation of  epidermal keratinocytes while  depleting mesenchymal stem  cells  (MSCs), which play  a crucial role in immune modulation. The progression behavior of psoriasis is not only  influenced by their present  state but also by  the historical evolution of underlying  cellular interactions. Memory stages  and complex interplay  among   immune  components at  different   temporal   scales  significantly  modulate disease expression. Motivated by this, we proposed a mathematical model  of psoriasis to a fractional-order framework in  order  to  incorporate  memory-dependent  effects  and  non-local  characteristics.   This   article  deals  with   a four-dimensional  model  of  psoriasis involving  concentrations of  T  cells,  dendritic cells,  keratinocytes, and mesenchymal stem  cells  (MSCs) in  order  to predict  the  temporal   evolution in  the  considered cell  densities during the  disease  dissemination process.     Using  Caputo, Caputo-Fabrizio, and  Atangana-Baleanu-Caputo operators,   we  analyze  how   memory  influences disease   dynamics.    In-depth  mathematical analysis  of  the solution of  the  fractionalized  model   has  been  thoroughly  investigated.   The  stability of  the  model   is  also examined using generalized Ulam–Hyers stability criteria.  The considered population densities  are numerically evaluated using  various  fractional orders  with   considered  fractional  operators  to  capture  non-local effects. Optimal control  is  implemented on  the  fractionalized system  using the  Forward-Backward Sweep  Method (FBSM), emphasizing the impacts  of two biologics, namely TNF-α inhibitors and IL-23 blockers,  via  considered operators.  Numerical simulations are performed in support of the theoretical analyses, accompanied by detailed discussions from  both mathematical and  biological viewpoints.  Results based on optimal control  effectiveness analysis indicate  that a combined control  strategy,  particularly under  the Caputo-Fabrizio operator,  optimally reduces  keratinocyte density.  Which offers  deeper  insights into  disease  progression and  effective  therapeutic approaches.
Mathematical Analysis on the Effects of Microplastic Pollution and Ocean Acidification on Coral Reefs in Aquatic Ecosystem Rahman, MD Shakilur; Mallick, Uzzwal Kumar
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.30288

Abstract

This study explores the complex interplay between microplastic contamination and ocean acidification in influencing coral reef ecosystems through the development of a mathematical model with time-varying parameters.  The model ensures positivity and boundedness to accurately represent ecological dynamics, and stability analyses provide insights into system behavior under various environmental conditions.  Numerical simulations validate the theoretical results and reveal that microplastic accumulation in marine environments significantly hinders coral reef establishment while contributing to elevated oceanic carbon dioxide levels. These rising CO2  levels, primarily driven by anthropogenic emissions, lead to accelerated ocean acidification, further degrading coral reefs. Model predictions indicate that, if unchecked, the current trends in microplastic pollution and ocean acidification will result in a 50% reduction in coral reef coverage within  four decades. However, the findings suggest that limiting microplastic input into aquatic ecosystems could  mitigate these adverse effects, preserving reef health and slowing acidification.   By quantifying the  relationship between microplastic pollution, ocean acidification, and coral reef dynamics, this study provides a robust framework for understanding and addressing critical threats to marine ecosystems.
Optimizing Algal Bloom Through Bioenzyme and Harvesting Control for Bioenergy Purposes in Eutrophic Water Bodies Akbar, Fadilah; Mardlijah, Mardlijah
Jambura Journal of Biomathematics (JJBM) Volume 5, Issue 2: December 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v5i2.26938

Abstract

This article discusses the optimization of algae growth for bioenergy purposes in eutrophic water bodies through bioenzyme control and harvesting. The study explores innovative approaches to manage algae growth in such water bodies. A mathematical model based on dynamical systems, specifically the NASC (Nutrients, Algae, Detritus, and Dissolved Oxygen) algae growth model, was used for the analysis. The results indicate that the system used is unstable, given the needs of algae growth over time. To optimize algae growth, this study proposes controlling the bioenzyme (u1) feeding to decompose detritus into nutrients and harvesting algae using (u2). The Pontryagin Maximum Principle (PMP) method was used to obtain optimization with control parameters u1=0.093 and u2=0.32. The results show that the optimal time to harvest algae is every 84 days or 2.8 months, with an estimated harvestable amount of 16.3667  . This discovery enhances our understanding of controlling algae growth in the context of renewable energy and reinforces the mathematical approach to managing eutrophic aquatic ecosystems.
A Fractional Mathematical Model for Controlling and Understanding Transmission Dynamics in Computer Virus Management Systems Yunus, Akeem Olarewaju; Olayiwola, Morufu Oyedunsi; Ajileye, Adewole Mukaila
Jambura Journal of Biomathematics (JJBM) Volume 5, Issue 2: December 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v5i2.25956

Abstract

The constant danger of computer viruses and malware makes it difficult to safely simulate the management of computer systems over time for both networks and individual users. The present study proposes a novel six-compartment fractional model that builds on existing classical frameworks and examines the existence and uniqueness of its solution, indicating that it is both mathematically and biologically well-posed. Additionally, we compute the fundamental reproduction number R0 and use sensitivity analysis to investigate the impact of various factors on the model's behavior. The Laplace Adomian Decomposition Method is employed for numerical analysis, and its findings have the potential to transform computer security and network management by providing robust countermeasures and eradication tactics. The complex properties of the fractional-order model are further explored by examining the memory effect of fractional order on system dynamics. The research findings offer valuable insights for virus managers in developing and implementing effective management methods and can successfully prevent the spread of computer viruses by leveraging these discoveries. In conclusion, this study provides significant insights and solutions for protecting the integrity of digital domains and network infrastructure.
Forward and Backward Bifurcation Analysis From an Imperfect Vaccine Efficacy Model With Saturated Treatment and Saturated Infection Fatahillah, Hakan Ahmad; Aldila, Dipo
Jambura Journal of Biomathematics (JJBM) Volume 5, Issue 2: December 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v5i2.28810

Abstract

This paper aims to study the saturation effect on the infection and recovery process within a Susceptible-Vaccination-Infected model featuring an imperfect vaccine efficacy. First, we nondimensionalized the model under the assumption of a constant population, resulting in the reduction of the model from three to two-dimensional differential equations. The analysis indicates the presence of a disease-free equilibrium (DFE) and potentially multiple endemic equilibria (EE) within the model. The calculation of the basic reproduction number further explains the model's solution conditions. In particular, we discovered that a backward bifurcation is possible under specific saturation effect values. Dulac's criterion confirmed the absence of a closed orbit in the solution region, suggesting the global stability of the endemic equilibrium when the basic reproduction number exceeds one. To supplement the analytical study, a numerical simulation was conducted to generate a bifurcation diagram, autonomous simulation, and global sensitivity analysis. The global sensitivity analysis revealed that changing the vaccination rate or recovery rate could significantly impact the basic reproduction number. Moreover, the bifurcation diagram depicting the relationship between the transmission rate and vaccination rate demonstrated that increasing the vaccination rate while maintaining the transmission rate can reduce the proportion of infected individuals within the population.
Dynamic Simulations of Parkinson’s Disease and ALS Propagation in the Brain: A Reaction-Diffusion on Brain Networks Approach Arsyad, Zaidan Al; Munir, Faiz; Allawiyah, Amanda; Sania, Tiara; Hartawan, Irvan; Putra, Prama
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 4: December 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i4.30557

Abstract

Neurodegenerative diseases,  such  as  Parkinson’s Disease  (PD)  and  Amyotrophic Lateral  Sclerosis  (ALS), progressively degrade neural systems, leading to considerable cognitive functional disorders. It is consequential to comprehend how these diseases spread within the brain to develop precise diagnoses and interventions. This study uses a reaction-diffusion model on a human brain network model to explore and replicate the actual dynamic progression of PD and ALS. By incorporating diffusion processes with empirical brain network data, we simulated the disease’s progression through various regions. Our results show unique propagation patterns for PD and ALS and the effect of different network models on disease transmission.  The structure of brain neural networks plays a vital function in neurodegeneration and offers insights that lead to early detection and focused treatments. This study presents a potential viewpoint on handling and interfering in neurodegenerative diseases by perceiving their dependency on brain neural connectivity.
Dynamical Properties of HIV/AIDS Model with Saturated Treatment Beay, Lazarus Kalvein; Ilwaru, Venn Yan Ishak; Ansori, Muhammad Fandi; Bakarbessy, Lusye; Dahoklory, Monce; Saija, Maryone
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 4: December 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i4.34090

Abstract

Mathematical models are crucial for developing control strategies,  understanding disease transmission dynamics, and solving real-world problems. In this study, it applied a novel approach to the HIV/AIDS model. The saturated treatment was essentially used to model HIV/AIDS. A significant analysis of the HIV/AIDS epidemic model was presented, incorporating the new parameter. The mathematical analysis conducted on the model involved the examination of its boundedness,  determination of its equilibria, calculation of the HIV/AIDS reproductive number, and assessment of the stability of these equilibria.  The verification of the convergence analysis confirmed the effectiveness of the proposed scheme. The numerical results and simulations of the HIV/AIDS model are shown.  Biologically, we have conducted investigations to determine the effect of several parameters on the dynamics of HIV/AIDS transmission.
Energy and Laplacian Energy of Pythagorean Intuitionistic Fuzzy Graphs with Applications in Medical Diagnosis Networks Saravanakumar, Anitha; Periyannan, Jayalakshmi; Dhandapani, Prasantha Bharathi; Yahya, Nisky Imansyah
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 4: December 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i4.33977

Abstract

This study extends fuzzy graph energy analysis by introducing energy and Laplacian energy for Pythagorean Intuitionistic Fuzzy Graphs (PIFGs), a powerful generalization of intuitionistic fuzzy graphs capable of representing higher degrees of uncertainty. A novel connection matrix for PIFGs is defined, and new formulations for energy and Laplacian energy are established, along with sharp lower and upper bounds. Beyond theoretical contributions, the approach is applied to medical diagnosis networks, where vertices represent symptoms,  diagnostic tests,  and diseases,  and edges encode Pythagorean intuitionistic fuzzy relationships. These measures quantify both the overall strength of associations (energy) and their structural irregularity (Laplacian energy), offering interpretable indicators for diagnostic certainty or ambiguity.  The framework provides a robust mathematical basis for decision-making in biomedical contexts where data are uncertain, imprecise, or conflicting.

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