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Submitted
28 September 2022
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29 November 2022
Published
30 November 2022
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Abstract

Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. It is characterized by deterioration of memory, language, problem-solving, and other cognitive skills that affect a person's ability to perform everyday activities. This decrease occurs because nerve cells (neurons) in parts of the brain involved in cognitive function are damaged and stop working properly. One way to detect Alzheimer’s is to use models of machine learning algorithms. In this study, the authors' team aimed to compare models of machine learning algorithms to find the one that gives better results in  prediction Alzheimer's disease. Machine learning models algorithms in this study were built using Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machines, and Naive Bayes. The author's team then tested his 373 Alzheimer's disease patient data from Kaggle Open Datasets and showed that the Logistic Regression algorithm model can achieve better with 85,71% accuracy rate.

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biographies

Firman Akbar, STMIK Amik Riau

STMIK Amik Riau

Rahmaddeni, STMIK Amik Riau

STMIK Amik Riau
How to Cite
Firman Akbar, & Rahmaddeni. (2022). Komparasi Algoritma Machine Learning Untuk Memprediksi Penyakit Alzheimer. Jurnal Komputer Terapan, 8(2), 236–245. https://doi.org/10.35143/jkt.v8i2.5713
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