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All Journal EduMatika: Jurnal MIPA
May Rani Tabitha Sinaga
Universitas Negeri Medan
Chemistry Education Mathematics Physics

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Development of Deep Learning Model Based on Convolutional Neural Network (CNN) for Brain Tumor Classification Using MRI Images May Rani Tabitha Sinaga; Bungaria Tampubolon; Nurfitri Humayro Daulay; Dinie Triana; Aulia Hani; Ferdyanto Abangan Simanjuntak; Arnita Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1105

Abstract

Brain tumor classification using MRI images presents a critical challenge in medical radiology. This study develops a deep learning model based on Convolutional Neural Network (CNN) to classify brain MRI images into four categories: Normal, Glioma, Meningioma, and Pituitary. A publicly available dataset from Kaggle consisting of 20,672 images was used, with preprocessing and data augmentation applied. The model architecture includes convolutional, pooling, flatten, dense, and dropout layers, optimized using the Adam optimizer and categorical crossentropy loss function. The evaluation results show that the model achieved an overall accuracy of 96% with high f1-scores across all classes, particularly for the Pituitary class (0.98). The main contribution of this study lies in the integration of diverse data augmentation techniques and Explainable AI (XAI) methods, enabling the visualization of key areas in MRI images that support classification decisions. The proposed model is not only accurate but also demonstrates strong generalization and interpretability, making it a promising tool for clinical decision support systems in brain tumor diagnosis.
Comparison of Cox Proportional Hazards and Weibull Regression Models in Survival Analysis of Heart Failure Patients Using UCI Repository Data Ardicha Appu Sianturi; Risca Octaviyani Hutapea; May Rani Tabitha Sinaga
EduMatika: Jurnal MIPA Vol. 5 No. 3 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i3.1327

Abstract

Heart failure is a leading cause of death worldwide, with a high mortality rate due to decreased heart function and systemic complications. Survival analysis is used to understand factors that influence patient survival and estimate the risk of death based on clinical characteristics. This study aims to analyze factors that influence survival time in heart failure patients and compare the performance of the Cox Proportional Hazards (CoxPH) model with the Weibull Accelerated Failure Time (AFT) in predicting the risk of death. Data are from the Heart Failure Clinical Records Dataset (UCI Repository) which includes 299 patients with variables such as age, anemia, hypertension, serum creatinine levels, and ejection fraction. The analysis was performed using the Kaplan–Meier, CoxPH, and Weibull AFT methods with evaluation through AIC and C-index values. The results show that age, anemia, hypertension, and creatinine increase the risk of death, while ejection fraction is protective. The CoxPH model performed better (AIC 958.46; C-index 0.741) than the Weibull AFT (AIC 1282.24; C-index 0.259). Therefore, CoxPH is recommended for estimating relative risk between patients, while Weibull AFT is more suitable for estimating absolute survival duration.
Co-Authors Ardicha Appu Sianturi Arnita Arnita Aulia Hani Bungaria Tampubolon Dinie Triana Ferdyanto Abangan Simanjuntak Nurfitri Humayro Daulay Risca Octaviyani Hutapea