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All Journal International Journal of Artificial Intelligence Research Jurnal Ilmu Fisika
cindyawati, cindyawati
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Astronomy Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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GLCM Texture Feature Selection for Alzheimer's Detection: A Combination of Statistical Tests, Decision Tree, and Random Forest Cindyawati, Cindyawati; Suryantari, Risti; Sulungbudi, Janto V.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1509

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that leads to a gradual decline in memory and cognitive function, most commonly affecting individuals over the age of 65. Early detection is essential to enable timely interventions, slow disease progression, and improve quality of life. This study aimed to identify the most dominant texture features from brain MRI images using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction. The extracted features were analyzed through non-parametric statistical tests and machine learning algorithms, including Decision Tree and Random Forest, and validated with cross-validation procedures to ensure robustness. The findings revealed that contrast at 90° consistently emerged as the most significant feature, capturing vertical texture variations associated with brain atrophy, while correlation at 135° provided additional discriminatory power by representing disrupted pixel intensity relationships. In combination, these features enhanced the accuracy of classification models, outperforming other GLCM parameters. The results emphasize that careful selection of texture features improves both accuracy and stability in distinguishing between Alzheimer’s and non-Alzheimer’s brains. This study demonstrates that image-based machine learning frameworks can serve as reliable tools to support early detection of Alzheimer’s disease, offering valuable implications for clinical practice and guiding future research on efficient, non-invasive diagnostic approaches.
Model Kinetik Amyloid-Beta (Aβ) pada Penyakit Alzheimer Menggunakan Metode Euler dan Runge-Kutta Order ke-4 Cindyawati, Cindyawati; Ahmad, Faozan; Hardhienata, Hendradi; Kartono, Agus
Jurnal Ilmu Fisika Vol 17 No 2 (2025): September 2025
Publisher : Jurusan Fisika FMIPA Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jif.17.2.125-134.2025

Abstract

Alzheimer's disease (AD) is a neurological disease that causes decreased brain function. It is known that the accumulation of amyloid-beta (Aβ) plaques in the brain is one of the causes of AD. The accumulation of Aβ plaques in the brain is a dynamic process; it begins with the growth of amyloid-beta monomers (M1). Furthermore, amyloid-beta dimers (M2) and so on, so that this collected into oligomers (O), fibrils (P), and plaques in the brain. This disrupts the communication pathways between nerve cells. In this study, each process of amyloid-beta plaque accumulation is presented with a mathematical model in the form of an ordinary differential equation. Therefore, the coupled ordinary differential equations are given for the entire process of Aβ plaque accumulation. In this study, this coupled model is calculated using numerical methods, such as the Euler and fourth-order Runge-Kutta methods. The Euler methods is simple and efficient, but its accuracy is low and can accumulate errors with larger step sizes. The fourth-order Runge-Kutta methods offers higher accuracy, better numerical stability, and greater control over the accuracy of the solution. These two numerical methods have never been compared for estimating numerical solutions of coupled ordinary differential equations.
Co-Authors Agus Kartono Ahmad, Faozan Hardhienata, Hendradi Risti Suryantari Sulungbudi, Janto V.