Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Nasional Teknologi Komputer
Irwansyah Putera Sitorus
Universitas Pembangunan Panca Budi
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Tingkat Demensia Alzheimer’s Berbasis Machine Learning dan Deep Learning Salwa Nur JB; Fachrurazy; Irwansyah Putera Sitorus; Katharina Tyas Aprilia
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline, particularly in memory and reasoning abilities [1]. Early detection of disease severity plays a critical role in improving clinical decision-making [4]. This study aims to classify Alzheimer’s dementia levels using a public MRI dataset from Kaggle consisting of four classes: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented [11]. This research compares conventional machine learning methods, namely Decision Tree and Random Forest, with a deep learning approach using Convolutional Neural Network (CNN) [4][8]. The experimental stages include preprocessing, data splitting, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix [4][10]. The results show that Random Forest outperforms Decision Tree with an accuracy of 89%, while CNN achieves the highest performance at 92% [2][3][4]. These findings indicate that CNN is more effective in extracting spatial features from MRI images compared to traditional machine learning methods [2][3][9].
Co-Authors Fachrurazy Katharina Tyas Aprilia Salwa Nur JB