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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Imajiner: Jurnal Matematika dan Pendidikan Matematika Journal of Innovative and Creativity
Muniroh, Muna Afdi
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Optimizing COVID-19 Epidemiological Models: A Particle Swarm Approach to Parameter Estimation Muniroh, Muna Afdi; Sari, Sekar; Indrati, Dika Agustia
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

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Abstract

The SEIR (Susceptible-Exposed-Infectious-Recovered) mathematical model, represented as a system of nonlinear differential equations, has proven to be a powerful tool to describe the dynamics of the spread of infectious diseases such as COVID-19. The accuracy of the projection and understanding of this model relies heavily on the proper estimation of its parameters, such as transmission rate, incubation rate, natural birth rate, natural death rate, disease mortality rate, and recovery rate. This study focuses on the development and application of a new approach to estimate crucial parameters in the SEIR model by utilizing Particle Swarm Optimization (PSO). PSO is a metaheuristic optimization algorithm inspired by the social behavior of flocks of birds or schools of fish. PSO was chosen for its outstanding ability to find a global minimum in a complex search space, as well as its efficiency in handling nonlinear optimization problems. The advantage of PSO lies in its effective memory capacity, which allows the storage of previous best values, both individually and globally, thus accelerating convergence to the optimal solution. Through a simulation program, this study successfully identified the optimal set of parameters for the SEIR model. These estimated parameters were then carefully evaluated by comparing the model simulation outputs with available COVID-19 epidemiological data, demonstrating the model's ability to accurately replicate pandemic trends. The results of this study are expected to make a significant contribution to modelling and understanding the spread of COVID-19.
Model Matematika Dinamika Perilaku Bullying dengan Intervensi Sekolah dan Resiliensi Siswa Ilmi, Noraniza Bahrotul; Muniroh, Muna Afdi; Hastari, Ratri Candra; Prasetya, Nizarkasyi Fighar
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 7, No 6 (2025): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v7i6.24956

Abstract

In this study, a model of bullying behavior transmission was developed by incorporating school interventions and student resilience. Finding the equilibrium point, figuring out the basic reproduction number , and assesing the equilibrium point’s stability are all part of dynamical analysis. Two equilibrium points are identified, namely the bullying-free equilibrium and the bullying-present equilibrium.. The dynamical analysis result shows that the bullying- free equilibrium point is locally asymtotically stable if , conversely, when  the bullying-present equilibrium point is locally asymtotically stable. The numerical simulations result are consistent with the analytical result.
Modeling the Dynamics of Tuberculosis-Diabetes Mellitus Coinfection with an Optimal Control Approach Muniroh, Muna Afdi; Oktafianto, Kresna; Kurniawati, Eriska Fitri
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37539

Abstract

Tuberculosis–Diabetes Mellitus (TB-DM) coinfection poses a significant public health challenge, contributing to increased morbidity, treatment failure, and healthcare costs. This study develops a mathematical model that stratifies TB infection into latent and active classes to examine the dynamics of TB–DM coinfection. The model is analyzed to determine the disease-free and endemic equilibrium points, supported by numerical evaluation using established parameter values. Furthermore, prevention strategies are assessed through an optimal control framework, formulated using Pontryagin’s Minimum Principle and validated through numerical simulations. The findings are expected to provide effective strategies to reduce TB-DM transmission and minimize optimal control costs, thereby supporting the development of improved public health policies.
A VGG16 CNN-based Method for Multiclass Lung Cancer Classification using CT Imaging Sari, Sekar; Muniroh, Muna Afdi; Apriandy, Kevin Ilham
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1483

Abstract

Lung cancer is the leading cause of death worldwide among all types of cancer. Early detection and accurate classification are essential to prevent disease progression and improve patient survival rates. One effective approach is the use of computer-aided diagnosis (CAD) systems based on medical imaging, particularly CT scans, which provide high-resolution and non-invasive visualization of lung structures, including blood vessels, soft tissues, and lesions or nodules. This study proposes a VGG16 CNN-based multiclass classification method for lung cancer. Unlike previous studies that primarily focus on binary classification, this research addresses four distinct classes of lung nodule CT images to better reflect complex clinical needs. The modified VGG16 architecture incorporates additional layers, including Flatten, Dense, and Dropout, along with the Softmax activation function, effectively improving classification performance and reducing overfitting risk. An ablation experiment was also conducted by replacing ReLU with LeakyReLU to address the potential “dying ReLU” issue. However, the results indicated that LeakyReLU did not provide significant improvement over the standard ReLU. The proposed model achieved an accuracy of 90.72%, precision of 91.5%, sensitivity of 89.25%, specificity of 96.76%, F1-score of 90%, and a low loss value of 0.37. Furthermore, the modified VGG16-CNN outperformed other CNN architectures, including ResNet50, EfficientNetB1, MobileNetV2, and AlexNet, in multiclass lung cancer image classification. The results demonstrate that the proposed method is effective for diagnosing lung nodules from CT scans and has the potential to support medical professionals in making accurate and timely diagnoses.
Co-Authors APRIANDY, KEVIN ILHAM Eris Tri Kurniawati Hastari, Ratri Candra Ilmi, Noraniza Bahrotul Indrati, Dika Agustia Oktafianto, Kresna Prasetya, Nizarkasyi Fighar Sari, Sekar