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Hasan S Panigoro
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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 96554, Gorontalo, Indonesia
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Jambura Journal of Biomathematics (JJBM)
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27230317     DOI : https://doi.org/10.37905/jjbm
Core Subject : Health, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Public Health
The Jambura Journal of Biomathematics JJBM is a peer reviewed academic journal published by the Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo, Indonesia. The journal is established with the vision of becoming a leading scientific publication in Southeast Asia and serves as a platform for researchers, academicians, and practitioners to publish original research articles and review papers. JJBM focuses on explaining complex biological phenomena through mathematical approaches and acts as a bridge between theoretical mathematics and the life sciences. JJBM has a broad and interdisciplinary scope covering various research areas. The journal welcomes high quality submissions involving mathematical analysis, computational modeling, and statistical methods to generate biological insights. The main areas include population dynamics, evolutionary dynamics, epidemiology, infectious disease modeling, systems biology, ecological modeling, and optimal control in biological systems. Through this scope, JJBM aims to support innovation in both mathematics and biological applications. To ensure scientific quality and originality, every submitted manuscript undergoes a single blind peer review process by experts in the field. The journal applies a strict policy against plagiarism and uses tools such as Turnitin to ensure originality. All accepted manuscripts are required to be prepared using LaTeX to maintain consistency and quality in mathematical formatting. JJBM is published quarterly in March, June, September, and December. The journal follows an open access policy, allowing all published articles to be freely accessed by the public. This approach supports wider dissemination of knowledge and increases the visibility and impact of published research. JJBM is committed to publication ethics and accessibility. The journal follows the guidelines of the Committee on Publication Ethics COPE. To support inclusivity, JJBM provides waiver options for article processing charges for authors from low and lower middle income countries.
Arjuna Subject : Matematika - Matematika Terapan
Articles 12 Documents
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Mathematical Modeling of COVID-19: An Optimal Control Approach Ayodeji Sunday Afolabi; Abdulwahab Ridwan
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.2

Abstract

A non-linear mathematical model is developed to describe the transmission dynamics of COVID-19. The model’s well-posedness is verified by analyzing the positivity and boundedness of its solutions. Analytical expressions for the disease-free equilibrium points are derived and the stability analyses of the disease-free and endemic equilibrium points are conducted. A sensitivity analysis of the model parameters with respect to the basic reproduction number (R0) is carried out to identify the key factors influencing COVID-19 transmission. Consequently, the model is extended into an optimal control problem by incorporating three time-dependent interventions: preventive measures (such as travel restrictions and personal protection), continuous vaccination of susceptible individuals, and testing, isolation, and treatment of infected cases. Four control strategies, each combining at least two interventions, are explored. The autonomous and non-autonomous systems are analyzed. Numerical simulations indicate that implementing the three control measures concurrently provides the most effective strategy to mitigating the spread of COVID-19.
Analyzing Dengue Transmission Through a Two-Age-Class Host Population Model Eminugroho Sari; Dwi Lestari; Nikenasih Binatari; Retno Subekti; Fitriana Saptaningtyas
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.6

Abstract

Age is an important risk factor for vector-borne diseases such as dengue. Children are more exposed to mosquito bites than adults due to behavioral and environmental factors. This study extends the classical host-vector modeling framework by incorporating the human population divided into two age classes, i.e, children and adults. The model also introduces additional key biological parameters, such as $b$, the number of mosquito bites per day; $\sigma$, the intrinsic growth rate of the mosquito population; and $\eta$, the relative probability that a mosquito bites an adult rather than a child. We derive the basic reproduction number using the next-generation matrix method and analyze the local stability of the disease-free equilibrium. Furthermore, we obtain sufficient conditions for the local asymptotic stability of the endemic equilibrium in a specific case. Sensitivity analysis is discussed to identify parameters that have the most influence. The numerical simulations are provided to support the theoretical results.
Anaysis of Predator-Prey Dynamics Using Holling Type I & II Response Functions with Kleptoparasitism and Anti-Predator Behavior Tassha Putri; Dian Savitri
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.8

Abstract

Predator-prey interactions involving 3 species in an African forest ecosystem between Deer, Hyena and Lion considering the influence of kleptoparasitism and anti-predator behaviour using Holling type I $\&$ II functional responses. This predator prey model is constructed based on the assumption that the behaviour of the second predator Hyena often has the ability to defend itself against other predators such as fleeing, fighting, and intimidating which is called anti-predator behaviour. Based on the existing phenonema, the objectives of this study are to determine the model construction, equilibrium point analysis and stability, as well as numerical simulation and interpretation of the prey-prey model using Holling type I $\&$ II functional responses in the presence of kleptoparasitism and anti-predator behaviour. The calculation analysis in this study was carried out by finding the equilibrium point and stability analysis. The results of the dynamic analysis show that there are five equilibrium points with the type of stability, namely $E_1(x, y, z) = (0, 0, 0)$ which states the extinction of the three populations, point equilibrium $E_2(x, y, z) = (K, 0, 0)$ which represents the extinction of the first predator and second predator populations, point equilibrium $E_3(x, y, z)$ $=$ $\left(-\frac{\theta_2}{\theta_2b-\mu_2},0,-\frac{r\mu_2(K(\beta\theta_2-\mu_2)+\theta_2)}{K\beta_2(b\theta_2-\mu)^2}\right)$ which expresses extinction in the first predator population, point equilibrium $E_4(x,y,z) = \left(\frac{\theta_1}{\mu_1},\frac{r(\mu_1K-\theta_1)}{\mu_1K\beta_1},0\right)$ which expresses extinction at the second predator, and equilibrium point $E_5(x^*,y^*,z^*)$ which states that all three populations can coexist. Numerical simulation results show the existence of double stability at points $E_4$ and $E_6$ when the parameter values $\mu_1 = 0.3, \mu_2 = 0.12$ and double stability occurs again at points $E_4$ and $E_3$ when the parameter variation values $\mu_1 = 0.3, \mu_2 = 0.158$.
Mathematical Modeling and Analysis on Nitrogen Fixation for Controlling Sunflower Oil Production Ronjit Mondal; Jannatul Puspo; Uzzwal Mallick
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.11

Abstract

Nitrogen fixation plays a vital role in enhancing crop yields, yet its inefficient utilization remains a critical challenge in sustainable agriculture. In sunflower oil production, imbalances in nitrogen availability can lead to reduced growth and oil quality, necessitating effective management strategies. To address these challenges, we have developed a newly proposed mathematical model using a system of ordinary differential equations that has been studied both analytically and numerically. Positivity and boundedness of the model's solution, stability at the equilibrium points, and characteristics with respect to state variables have been studied as some parts of the analytical solution. Also, the numerical solution has been done using the Runge–Kutta 4th order technique. The findings of this study reveal that both excessive and insufficient nitrogen application can negatively impact the expected oil production from sunflower crops as well as maintaining soil pH within the standard range, which is essential for rising oil yield. Furthermore, the results indicate that applying nitrogen fertilizer at half the recommended rate between $30^{th}$ and $40^{th}$ days, followed by the full rate between the $40^{th}$ and $50^{th}$ days, results in the same expected oil production as applying the full recommended amount evenly throughout the period $30^{th}$ to $50^{th}$ days. Therefore, this study emphasizes the need for precise nitrogen management methods to improve oil production.
Bibliometric Analysis Of Machine Learning Applications In EEG For Epileptic Seizure Diagnosis Using Biblioshiny: Trends And Conceptual Structures Amirul Aizad Ahmad Fuad; Ashraff Ruslan
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.12

Abstract

This study examines the research landscape of machine learning applications in EEG-based epileptic seizure diagnosis through bibliometric analysis. A total of 2,805 Scopus-indexed publications (1967--2024) authored by 9,003 researchers were analysed using Biblioshiny in R-Studio to explore publication trends, influential works, collaboration networks, and thematic developments. The analysis reveals a steady annual growth rate of $1.91\%$, with a significant increase in research activity after 2015 driven by advancements in deep learning techniques. While the field benefits from an average of 5.4 co-authors per document, international collaboration remains modest at $26.2\%$ of the total output. Support vector machines (SVMs), artificial neural networks (ANNs), and convolutional neural networks (CNNs) are widely used for seizure detection. However, challenges remain, including limited dataset diversity, real-world implementation barriers, and computational demands. The study finds that research output is concentrated among a few highly cited authors and journals, with fewer contributions from resource-limited regions. The findings indicate a need for broader collaborations, diverse datasets, and evaluation metrics that reflect clinical relevance rather than solely technical performance. Future research should explore explainable AI (XAI), wearable EEG technologies, and practical machine learning integration in clinical settings to improve accessibility and reliability. Addressing these challenges can enhance the impact of machine learning in EEG-based epilepsy diagnosis, leading to better patient outcomes. This bibliometric study provides a detailed, quantified overview of the field's progress, offering insights that can guide future research towards greater inclusivity, collaboration, and real-world applicability.
Critical waves in a nonlocal dispersion delayed susceptible-infected-confined-quarantined-recovered outbreak model with general incidence function Nidhal Ali; Rassim Darazirar; Sawsan Abed; Ahmed Mohsen; Ebenezer Bonyah
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.15

Abstract

We study a delayed nonlocal epidemic model that includes the effects of confinement and a generalized incidence function. The model takes into account the spatial movements of the population through a nonlocal dispersal kernel and the behavioral control through a confinement parameter. First, we calculate the basic reproduction number $R_0$ and show its explicit dependence on the confinement rate. Second, we determine the minimal wave speed $\zeta^*$ of traveling wave solutions connecting the disease-free equilibrium to the endemic equilibrium. We show that $\zeta^*$ is given by the principal root of the corresponding characteristic equation and that (i) no traveling wave exists for $\zeta<\zeta^*$, and (ii) traveling waves exist for all $\zeta>\zeta^*$. Moreover, we show that the minimal wave speed is a monotone decreasing function of the confinement rate. Our results are obtained through a combination of spectral theory, upper-lower solution methods, and monotone iteration schemes that are modified to account for the joint effects of delay and nonlocal dispersal. Numerical simulations confirm the analytical prediction of the minimal wave speed and illustrate the quantitative slowing effect induced by confinement. These results provide a rigorous mathematical characterization of how mobility, delay, and confinement jointly determine epidemic invasion and spatial propagation.
Cost-Effectiveness and Optimal Control of Hantavirus Transmission in Rodent Populations Mahmoud Moustafa
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.16

Abstract

In this paper, we formulate and analyze a deterministic optimal control model for the transmission dynamics of hantavirus infection in rodent populations and identify economically efficient intervention strategies. The model incorporates three time-dependent controls: rodent harvesting, transmission reduction, and alien-oriented control. By applying Pontryagin’s Maximum Principle, we derive the Hamiltonian, the adjoint system, and explicit characterizations of the optimal controls, leading to the corresponding optimality system. Numerical solutions are obtained using a forward-backward sweep algorithm. Simulation results demonstrate that combined interventions can substantially reduce the infected rodent population; however, under limited resources, selecting a cost-effective policy is crucial. A health-economic evaluation based on the average and incremental cost-effectiveness ratios (ACER and ICER) shows that the joint implementation of harvesting and transmission reduction provides the most effective strategy among the alternatives considered. We further present a local normalized sensitivity analysis, highlighting the parameters that most strongly influence the predicted infections averted. These findings support the adoption of integrated, rodent-focused interventions for mitigating hantavirus transmission and offer quantitative guidance for informed public health decision-making.
Optimal control of HIV transmission model with pre-ART counselling and treatment Mohamad Syafi&#039;i; Fatmawati; Ahmadin; Chidozie Chukwu
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.17

Abstract

HIV attacks CD4 cells of the immune system, leading to progressive immune deficiency. Antiretroviral therapy (ART) involves the use of HIV drugs to treat HIV infection and is administered daily to slow disease progression. This paper aims to develop and analyze a mathematical model of HIV transmission that incorporates pre-antiretroviral therapy counselling and HIV treatment to reduce the number of HIV-infected individuals with high-risk behaviours for HIV transmission. A nonlinear dynamical system is constructed, and model parameters are estimated from Indonesia’s annual HIV case data using a genetic algorithm method. The model exhibits two equilibrium points: the disease-free equilibrium and the endemic equilibrium. Stability analysis shows that disease-free equilibrium is globally asymptotically stable when the basic reproduction number is less than one. Optimal control theory is applied to a system that consists of two time-dependent controls, pre-antiretroviral therapy counselling and HIV treatment. Healthcare professionals provide pre-antiretroviral therapy counselling to help people with HIV understand the disease and the benefits of antiretroviral therapy. Pontryagin's maximum principle is employed to derive optimal control conditions. The optimal control problem is numerically solved using the forward–backward sweep method with a fourth-order Runge–Kutta scheme. Three potential strategies were developed and investigated in our simulation. Implementing the two combined controls could significantly reduce the number of HIV-infected individuals and improve overall disease control in the population.
Non-Linear Function Approximation for Predicting Binding Affinity of PPAR-Targeting Antidiabetic Compounds from Molecular Descriptors La Ode Aman; Widy Abdulkadir; Dizky Papeo; Ariani Hutuba; Teti Tuloli; Mohamad Mustapa; Yuszda Salimi; Hamsidar Hasan; Arfan; Aiyi Asnawi
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.18

Abstract

Diabetes mellitus remains a major global health challenge, necessitating the development of more effective therapeutic agents. The PPAR family plays a crucial role in regulating glucose and lipid metabolism, making it an important target for antidiabetic drug discovery. However, the identification of potent PPAR-targeting compounds is often limited by the high cost and time-consuming nature of experimental approaches. This study aims to develop a non-linear function approximation model to predict docking-derived binding affinity of antidiabetic compounds targeting PPAR using 2D molecular descriptors. A dataset of 3,764 small molecules with IC50 values was curated from the ChEMBL database, followed by data preprocessing to remove duplicates and incomplete entries. Molecular docking simulations were performed using AutoDock Vina to obtain binding affinity scores (kcal/mol), which were used as the target variable. Subsequently, 2D molecular descriptors were calculated from SMILES representations to capture key structural and physicochemical properties of the compounds. These descriptors were used as input features for a Multi-Layer Perceptron (MLP) regression model to approximate the complex non-linear relationship between molecular structure and binding affinity. The model achieved R² values of 0.853 for the training set and 0.632 for the test set, indicating moderate predictive performance and acceptable generalizability. Overall, this approach demonstrates the potential of machine learning as a cost-effective and scalable tool to support early-stage discovery of antidiabetic compounds targeting the PPAR family.
Study on Listeriosis Transmission Dynamics including Time Lag and Media-Driven Behavioral Change Kapil Toor; Kalyan Das
Jambura Journal of Biomathematics (JJBM) Vol. 7 No. 1: March 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v7i1.25

Abstract

Listeriosis is a foodborne disease caused by the bacterium Listeria monocytogenes, posing significant risks to public health due to its high mortality rate among vulnerable populations. This study develops a comprehensive mathematical model to analyze the dynamics of Listeriosis transmission, incorporating human populations, bacterial growth, food contamination, and the influence of media campaigns. The model divides the human population into compartments of unaware susceptible, aware susceptible, infected, and recovered individuals, while also tracking bacterial populations, media campaigns, and uncontaminated and contaminated food supplies. To capture realistic disease progression, the model includes a delay term $\tau$ to account for time lags in awareness campaigns and contamination processes. Stability analysis of the disease-free and endemic steady states is performed to identify critical thresholds for disease control. Additionally, the effects of delays on the system's stability and the potential for oscillatory dynamics are investigated. Sensitivity analysis is conducted to determine the influence of key parameters on disease dynamics. The model provides valuable insights into effective strategies for controlling Listeriosis and mitigating its impact on public health.

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