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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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Mathematical Modeling on the Transmission Dynamics of Diphtheria with Optimal Control Strategies Oguntolu, Festus Abiodun; Peter, Olumuyiwa James; Omede, Benjamin Idoko; Balogun, Ghaniyyat Bolanle; Ajiboye, Aminat Olabisi; Panigoro, Hasan S.
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.v6i1.29716

Abstract

Diphtheria is an acute bacterial infection caused by Corynebacterium diphtheriae, characterized by the formation of a pseudo-membrane in the throat, which can lead to airway obstruction and systemic complications. Despite the availability of effective vaccines, diphtheria remains a significant public health concern in many regions, particularly in areas with low immunization coverage. In this study, we formulated and rigorously analyzed a deter ministic epidemiological mathematical model to gain insight into the transmission dynamics of Diphtheria infection, incorporating the concentration of Corynebacterium Diphtheriae in the environment. The analysis of the model begins with the computation of the basic reproduction number and the examination of the local stability of the disease-free equilibrium using the Routh-Hurwitz criterion. An in-depth analysis of the model reveals that the model undergoes the phenomenon of backward bifurcation. This characteristic poses significant hurdles in effectively controlling Diph theria infection within the population. However, under the assumption of no re-infection of Diphtheria infection after recovery, the disease-free equilibrium point is globally asymptotically stable whenever the basic reproduction num ber is less than one. Furthermore, the sensitivity analysis of the basic reproduction number was carried out in order to determine the impact of each of the model basic parameters that contribute to the transmission of the disease. Utilizing the optimal control theory to effectively curb the spread of Diphtheria, We introduced two time dependent control measures, to mitigate the spread of Diphtheria. These time dependent control measures represent preventive actions, such as public enlightenment campaign to sensitize and educate the general public on the dynamics of Diph theria and proper personal hygiene which includes regular washing of hands to prevent susceptible individuals from acquiring Diphtheria, and environmental sanitation practices such as cleaning of surfaces and door handle to reduced the concentration of Corynebacterium diphtheriae in the environment. The results from the numerical simulations reveal that Diphtheria infection can successfully be controlled and mitigated within the population if we can increase the vaccination rate and the decay rate of Corynebacterium Diphtheriae in the environment, as well as properly and effectively implementing these optimal control measures simultaneously.
Identifying the Fetal Heart Rate and gender with Intuitionistic Fuzzy Total edge Magic Labelling Aruchamy, Pradeepa; Mahagaonkar, Pralahad; Ganesan, Gomathi; Dhandapani, Prasantha Bharathi; Yahya, Nisky Imansyah
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 2: June 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i2.30951

Abstract

The application of Intuitionistic fuzzy total edge magic labelling to a graphical image at the 20th week of gestation provides insights into facilitates gender prediction as well as assessment of fetal blood flow on a second-trimester Doppler ultrasound screen. Ultrasounds screen the fetal heart rate during the 20th week of gestation using Doppler ultrasound for blood flow. A fetal heart rate above 2.5 beats per second suggests a female baby, while a rate less than 2.5 beats per second indicates a male baby. We convert the fetal heart blood flow into a graphical image and label it using intuitionistic fuzzy total edge magic labelling.
Analysis of Five-Year Malaria Prevalence at the Federal Teaching Hospital, Ido-Ekiti, Nigeria Mmaduakor, Chika; Ngwu, Benitho; Ojo-Lawal, Sherifat; Oluwafemi, Glory; Peter, Olumuyiwa James; Raso, Mario
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 2: June 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i2.30958

Abstract

Malaria remains a major public health problem globally, with Nigeria accounting for approximately 27% of the global burden. Chronological analysis of malaria data is vital for evaluating the performance of malaria prevention programmes in Nigeria. Therefore, the objective of this study is to determine the malaria prevalence rate at the Federal Teaching Hospital, Ido-Ekiti (FETHI), over a five-year period. Data from 484 suspected malaria patients who visited the hospital between 2019 and 2023 were collected and analysed. Logistic regression was used to evaluate the relationship between positive blood film results and potential associated factors. Among all presumptive cases, 307 (63.4%) were female. The annual malaria prevalence ranged from 30.4% to 54.2%, with an overall prevalence of 42.32% (95% CI: 34.3%–54.4%). Two Plasmodium species were detected: Plasmodium falciparum (98 cases, 47.3%) and Plasmodium vivax (83 cases, 40.1%). A higher proportion of cases were recorded in December, January, and May (50%, 51.2%, and 51.4%, respectively). Patients who visited the hospital in January were twice as likely to be infected compared to those in April [OR: 2.29; 95% CI: 0.88–6.18; p = 0.037]. Males were half as likely to be infected as females [OR: 0.47; 95% CI: 0.30–0.72; p = 0.00066]. Malaria remains a significant concern in the studied location. Therefore, malaria control programmes need to be strengthened to reduce its impact.
Coastal Ecosystem Classification Using Satellite-Based Machine Learning Approaches Jane, Giani Jovita; Alifatri, La Ode; Tasriah, Etjih; Pramana, Setia
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 2: June 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i2.30466

Abstract

Sebagai negara kepulauan yang kaya akan sumber daya alam, Indonesia memiliki potensi ekonomi kelautan yang besar. Untuk mempertahankan potensi ekonomi ini dalam jangka panjang, ekonomi biru diperlukan sebagai konsep dalam menetapkan program pembangunan dan kebijakan publik. Salah satu cara untuk mengimplementasikan konsep tersebut adalah dengan menyusun neraca laut, yang kerangka kerjanya mengimplementasikan konsep ekonomi biru dalam bentuk neraca lingkungan. Neraca laut dapat dianggap mendukung pembentukan kebijakan dan program nasional suatu negara. Oleh karena itu, data spasial yang akurat yang mencerminkan kondisi terkini sangat penting untuk menyusun neraca ini. Namun, pengumpulan data tersebut dapat memakan biaya dan sumber daya yang besar, sehingga menjadi tantangan untuk memastikan ketersediaan informasi yang terkini dan akurat. Dalam konteks ini, sumber data alternatif dapat memberikan solusi yang layak. Penelitian sebelumnya telah berhasil membuktikan bahwa pemodelan pembelajaran mesin juga citra satelit Sentinel-1 dan Sentinel-2 mampu memetakan wilayah pesisir, seperti wilayah pasang surut dan bentik. Oleh karena itu, penelitian ini mencoba mengklasifikasikan ekosistem pesisir Taman Nasional Karimunjawa dengan memanfaatkan citra Sentinel-1 dan Sentinel-2 dan membandingkan hasil klasifikasi dari tiga metode pembelajaran mesin, yaitu Random Forest (RF), Support Vector Classification (SVC), dan Extreme Gradient Boosting (XGBoost), dan menganalisis perubahan ekosistem antara tahun 2020 dan 2023. Hasilnya menunjukkan bahwa RF memberikan hasil terbaik dalam melakukan klasifikasi untuk daerah bentik yang mencapai 0,77 dan 0,78 dalam skor F1 dan Koefisien Korelasi Matthew (MCC), sedangkan model SVC berhasil mencapai 0,83 dalam skor F1 dan MCC memberikan hasil terbaik untuk daerah pasang surut. Selanjutnya, luas terumbu karang dan padang lamun menurun masing-masing sebesar 6,524 km 2 dan 1,39 km 2 . Sedangkan, luas mangrove, kawasan terbangun, dan hutan menunjukkan sedikit perubahan.
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.

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