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All Journal Zeta - Math Journal Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika
Muhammad Thahiruddin
Institut Sains Dan Teknologi Annuqayah
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Health Professions Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Penyelesaian Numerik Model Pemangsa-Mangsa dengan Metode Jaringan Fungsi Radial Basis Menggunakan Trigonometric Shape Parameter Muhammad Thahiruddin; Mohammad Jamhuri
Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika Vol. 1 No. 4 (2023): Agustus : Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/arjuna.v1i4.135

Abstract

One mathematical model in the form of a system of nonlinear ordinary differential equations is the predator-prey model. The predator-prey model explains population changes of one prey population and one predator population due to changes in time. The radial basis function network method is used to find a numerical solution to the predator-prey model. The radial basis function network method can directly approximate the function and derivative of the prey-prey model using a basis function. The basis function used is a multiquadric basis function. Numerical solutions using the radial basis function network method obtained from this research show high accuracy and low error. The absolute error obtained from the two simulations with Δt = 0.01 each is 0.0066 in the first simulation and 0.022 in the second simulation. The errors obtained are relatively small because each only represents 0.66% of the initial value of the first type and 0.5% of the initial value of the second type. This shows that the radial basis function network method is efficient in calculating the predator-prey model solution.
A Robustness Study of Multi-Layer Perceptrons and Logistic Regression to Data Perturbation: MNIST Dataset Thahiruddin, Muhammad; Khotijah, Siti; Fajar, Moh.; Farras, Adib El
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.39-50

Abstract

This study systematically evaluates the robustness of Multi-Layer Perceptrons (MLPs) And Logistic Regression (LR) models against data pertubations using the MNIST handwritten digit dataset. While MLPs and LR are foundational in machine learning, their comparative resilience to diverse pertubations-noise, geometric distortions, and adversarial attacks-remains underexplored,despite implications for real-world applications with imperfect data., whe test three pertubations categories : Gaussian noise (σ=0.1 to 1.0), salt and pepper noise (p=0.1 to 0.5), rotational distorsions (5° to 30°), and adversial attacks (FGSM with ϵ=0.005 to0.30). both models were trained on 60.000 MNIST samples and tested on 10.000 pertubed images. Results demonstrate that MLPs exhibit superior robustness under moderate noise and rotations, achieving baseline accuracies of 97.07% (vs. LR’s 92.63%). For Gaussian noise (σ=0.5), MLP retained 35.35% accuracy compared to LR’s 23.91% . however, adversarial attacks (FGSM, ϵ= 0.30) reduced MLP accuracy to 0.20%, revealing critical vulnerabilities. Statistical analysis (paired t-test, p < 0.05) confirmed significant performance differences across pertubations levels. Alinear regressions (R^2 = 0.98) further quantified MLP’s predictable accuracy decline with Gaussian noise intensity. These findings underscore MLP’s suitability for noise-prone environments but highlight urgent needs for adversarial defense mechanisms. Practitioners are advised to prioritize MLPs for tasks with moderate distortions, while future work should integrate robustness enhancements like adversarial training.
CNNs vs. Hybrid Transformers for Brain Tumor Classification on the BRISC Dataset Thahiruddin, Muhammad
Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Vol 6 No 1 (2025): Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM)
Publisher : Fakultas Teknik Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/jatim.v6i1.3545

Abstract

Accurate and timely classification of brain tumors from Magnetic Resonance Imaging (MRI) is critical for effective treatment planning. The advent of deep learning has revolutionized medical image analysis, yet the performance of different model architectures is highly dependent on the quality of benchmark datasets and the specifics of the training methodology. This study presents a rigorous comparative analysis of four prominent deep learning architectures—ResNet18, EfficientNet-B0, MobileNetV3-Small, and the hybrid convolutional-transformer model MobileViTV2-100—for multi-class brain tumor classification. The models were trained and evaluated on the BRISC dataset, a large-scale, balanced collection of 6,000 T1-weighted contrast-enhanced MRI scans, comprising glioma, meningioma, pituitary, and no-tumor classes. Employing a 5-fold cross-validation protocol with a full fine-tuning strategy and robust regularization techniques, this study assesses models on both classification accuracy and computational efficiency. The results indicate that MobileViTV2-100, ResNet18, and EfficientNet-B0 achieve statistically comparable state-of-the-art performance, with mean test accuracies of 98.88%, 98.72%, and 98.72%, respectively. MobileNetV3-Small, while being the most parameter-efficient, demonstrated significantly lower accuracy at 96.94%. A key finding reveals a performance-efficiency paradox, where the largest model, ResNet18, exhibited the fastest inference latency (2.83 ms), challenging the conventional assumption that fewer parameters directly translate to greater speed. This comprehensive analysis underscores the strengths of hybrid architectures and provides critical insights into the practical trade-offs between model complexity, accuracy, and real-world deploy ability for clinical decision support systems.
Co-Authors Fajar, Moh. Farras, Adib El Mohammad Jamhuri Siti Khotijah