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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Imajiner: Jurnal Matematika dan Pendidikan Matematika
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 Other

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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.
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 Hastari, Ratri Candra Ilmi, Noraniza Bahrotul Prasetya, Nizarkasyi Fighar Sari, Sekar