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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 10 Documents
 1 
Search results for , issue "Volume 6, Issue 3: September 2025" : 10 Documents clear
Machine Learning Model for Predicting the Temporal Lassa Fever Confirmed Cases in Nigeria Adekunle, Taiwo A.; Ogundoyin, Ibrahim K.; Akanbi, Caleb O.
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.v6i3.33831

Abstract

Lassa fever continues to pose a major public health threat in Nigeria, marked by recurrent outbreaks and high case fatality rates. The absence of robust predictive models has significantly impeded accurate trend forecasting, thereby limiting timely resource deployment and the implementation of effective preventive measures. This study seeks to bridge that gap by developing a comprehensive predictive framework for estimating confirmed Lassa fever cases in Nigeria. The research utilizes a combination of quantitative analysis and computational modeling techniques, leveraging weekly epidemiological data on Lassa fever cases from the Nigeria Centre for Disease Control (NCDC), spanning the year 2020. The dataset, which includes both suspected and confirmed cases, was cleaned and restricted to confirmed cases for the purpose of this analysis. Key steps included feature selection and dimensionality reduction to enhance model efficiency and accuracy. Three predictive models—Random Forest, Linear Regression, and Gradient Boosting—were developed and assessed using standard evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). These models were designed to forecast future confirmed Lassa fever cases. The findings highlight the critical role of temporal variables, particularly weeks and months, in shaping transmission patterns. These features were shown to significantly influence the trends in confirmed cases.
Reconstruction of the Phi-2 Method for Question-Answering Related to Diabetes Disease Using the MedAlpaca Dataset Ridho, Muhammad; Bustamam, Alhadi; Adnan, Risman
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.v6i3.30506

Abstract

This  study  focuses on the reconstruction of the Phi-2  method  for text-based question-answering systems  related to diabetes  using the MedAlpaca dataset.   The  aim  is to enhance  the accuracy in  diabetes  question-answering applications.   We  leverage LoRA  techniques   to fine-tune  the model,  thereby  improving its  ability to handle complex medical queries.  The integration of the MedAlpaca dataset, which contains  a diverse range of medical questions  and answers,  provides a robust  foundation for training and testing the model.  The results  reveal  that fine-tuning  with   MedAlpaca  significantly  enhances   the  model’s   performance,  achieving  higher   accuracy compared to the base Phi-2  model,  achieving a performance increase  from  14.81% to 49.37% on MedMCQA, reaching  92.83%  on  PubMedQA, and  38.78%  on  MedQA. It  also  surpasses  other  leading  models   such  as BioBERT  (89.90%)   and   GatorTron  (90.87%).        The   results    highlight  the   effectiveness    of   incorporating domain-specific datasets  like  MedAlpaca to boost model  performance.  This  advancement points  to promising directions  for  future  research,   including  expanding datasets  and  refining fine-tuning techniques   to  further improve automated  medical question-answering systems.
The Analysis of Epidemic Dynamical Models for Dengue Transmission Considering the Mosquito Aquatic Phase Inayah, Nur; Manaqib, Muhammad; Fitriyati, Nina; Wijaya, Madona Yunita; Fiade, Andrew; Sari, Flori Ratna
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.v6i3.29332

Abstract

This  study  generalizes the dengue  transmission model  by  considering the dynamics of the human population and  the Aedes  aegypti mosquito  population.  The  mosquito  population is  devided into  two  phases,  i.e.,  the aquatic  phase and the adult  phase.  From  the model,  we seek the disease-free  equilibrium, endemic  equilibrium, and  basic  reproduction number   (R0) points.    The  model  yields a  single   basic  reproduction number   which determines the system’s  behavior.   If  R0    1,  the disease-free  equilibrium is  locally  asymptotically stable, indicating that the disease  will die out.  Conversely, if R0    1, an endemic  equilibrium exists,  and  the disease may  persist  in the  population.    Next,   a  numerical simulation  is  performed  to  geometrically  visualize   the resulting analysis  and  also  to  simulate the  dengue   transmission in  DKI Jakarta   Province,  Indonesia.   The resulting  numerical simulation  supports our  analysis.   Meanwhile, the  simulation in  DKI Jakarta  Province suggests that  the dengue  fever  disappears after  60 days  from  the first  case appearance  after  controlling  the mosquito  population through fogging and the use of mosquito  larvae  repellent.  Lastly, the sensitivity analysis of R0   indicates  that  parameters   related  to  the  mosquito’s  aquatic   phase  have  a  strong   influence   on  dengue transmission, meaning that small  changes  in these parameters  can significantly increase or decrease the value  of R0  and thus the potential  for an outbreak.
Implementation of Moving Average Filter in SARIMA-ANN and SARIMA-SVR Methods for Forecasting Pneumonia Incidence in Jakarta Musyaffa, Muhammad Majid Rafi; Hertono, Gatot Fatwanto; 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.v6i3.30558

Abstract

In this study, we implemented a moving average filter in SARIMA-ANN and SARIMA-SVR to predict Pneumonia incidence in Jakarta. Pneumonia is one of the highest causes of death in children throughout the world. Forecasting pneumonia incidence in the future can help to reduce the spread of cases, so that the number of deaths due to pneumonia can be reduced. In general, time series data consists of linear and nonlinear patterns, which cannot be properly modeled by linear or nonlinear models alone. One way to solve this issue is to use a hybrid model that combines several models to overcome the limitations of each component model and improve predicting performance. SARIMA-ANN and SARIMA-SVR methods combine a linear seasonal autoregressive integrated moving average (SARIMA) model and a nonlinear artificial neural network (ANN) or support vector regression (SVR) model to capture the linear and nonlinear characteristics of the data. Parameter estimation in SARIMA uses Gaussian Maximum Likelihood Estimation. Initially, the time series will be transformed by a moving average (MA) filter, so SARIMA can model the data well. Meanwhile, the remaining components separated from the transformation will be modeled with a nonlinear model such as ANN in the SARIMA-ANN method, or SVR in the SARIMA-SVR method. The simulation results show that the SARIMA-ANN method is superior to the SARIMA-SVR method in predicting incidences in West Jakarta and East Jakarta, with a MAPE difference ranging from 0.6% to 0.75%. Meanwhile, in North, South, and Central Jakarta, the SARIMA-SVR method is superior to the SARIMA-ANN method, with MAPE differences ranging from 1.6% to 3.99%. The SARIMA-SVR model achieves better results across the majority of municipalities, indicating that the SARIMA-SVR model generally provides better result for predicting Pneumonia incidence in Jakarta.
The Effectiveness of B Cells in CAR T Cell Therapy for B Cells Acute Lymphoblastic Leukemia Haries, Elena M. D. P.; Abadi, Abadi
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.v6i3.32511

Abstract

Chimeric Antigen Receptor (CAR) T cell therapy has shown remarkable clinical outcomes in B cell Acute Lymphoblastic Leukemia (B-ALL). The treatment can utilize the immune system to recognize and kill leukemia cells through the CD19 antigen target.  However, the CD19 antigen is also expressed on normal B cells, which can cause side effects in B cell aplasia.  This study modifies a mathematical model of the interaction between CAR T cells, leukemia cells, and normal B cells by introducing the assumption that leukemia cells follow logistic growth dynamics. Determined the equilibrium point and continues to analyze stability using linearization and the Routh-Hurwitz criterion.  The analysis reveals four equilibrium points, including a state where leukemia cells grow at maximum capacity in the absence of CAR T cells.  Bifurcation analysis shows the occurrence of both transcritical and subcritical Hopf bifurcations, with distinct patterns compared to previous models.   A heteroclinic cycle was also identified, indicating that relapse may occur even after remission.   The logistic growth and B cell progenitors not only shape remission and relapse dynamics but also explain the dual role of B cells in sustaining CAR T activation and causing complications such as Cytokine Release Syndrome (CRS). This provides new insights for understanding therapy outcomes and optimizing CAR T cell treatment strategies.
Optimal Control and Model Analysis of The Spread of Pneumonia in Toddlers in East Java-Indonesia Using The Pontryagin’s Minimum Principle Widodo, Basuki; Kamiran, Kamiran; Syahputri, Denisa Dwi
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.v6i3.31974

Abstract

Pneumonia is  a  type  of  acute  respiratory  infection   (ARI) that  attacks  the  lungs and  is  caused   by  various microorganisms, such  as bacteria, viruses, parasites,  fungi, exposure to chemicals, or physical damage  to the lungs. Pneumonia is  included in  the list  of  10 diseases  with  the highest  number   of  cases  according to the Indonesian Ministry of Health reported  in April 2023. Pneumonia is the biggest cause of death in toddlers  aged 12-59  months,  reaching  12.5%. Therefore,   to  reduce  the  spread   of  pneumonia,  this  research  will  discuss providing  optimal control using the mathematical model  of  SEIR (Susceptible-Exposed-Infected-Recovered). The model  used is a pneumonia spreading model  with  implementing control in the form of first stage treatment and second  stage treatment. The results  of the stability analysis show  that at the disease-free  equilibrium point and  the endemic  equilibrium point,  the system  is  stable  respectively. Based  on  controllability analysis, it  is obtained  that the system  is controlled so that the system  can be controlled. In addition, based on the results  of the analysis of the optimal control  problem  with  Pontryagin’s Minimum Principle simulated with  Runge Kutta order  4, it shows  that the first  stage of treatment control  (u1)  and  the second  stage of treatment  (u2)  are very effective   in   reducing  the  number   of  individuals  infected   with   mild  pneumonia and   severe   pneumonia respectively.
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.

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