Abstract— Alzheimer's disease is a degenerative disease associated
with memory loss, communication difficulties, mental health,
thinking skills, and other psychological disorders that affect a
person's daily activities. Alzheimer's disease is a disease that
causes disability for people aged 70 years and over and is the
seventh highest contributor to death in the world. However, until
now there has not been found an effective treatment to cure
Alzheimer's disease. Thus, early detection of Alzheimer's disease
is very important so that sufferers of Alzheimer's disease can
immediately receive intensive medical care so as to reduce the
death rate from Alzheimer's disease. One method that can be used
to detect Alzheimer's disease is by utilizing a machine learning
algorithm model. The machine learning model in this study was
carried out using the Decision Tree C4.5 algorithm classification
method based on Binary Particle Swarm Optimization (BPSO).
The C4.5 Decision Tree algorithm is used to classify Alzheimer's
disease, while the BPSO algorithm is used to perform feature
selection. By performing feature selection with the BPSO
algorithm, the results show that the BPSO algorithm can improve
accuracy and can increase the performance of the C4.5 algorithm
in the Alzheimer's disease classification process. The results of the
accuracy of the C4.5 algorithm using the BPSO feature selection
are greater, namely 98.2% compared to the C4.5 algorithm
without BPSO feature selection, which is only 96.4%.