Application of the Random Forest Classifier Method in Grouping Patients with Intellectual Disabilities

Ainiyah, Nuchaila
Afifudin, Muhammad
Masyhuri, Reyhan Dela
Fardana, Muhamad Hakam
Wahyuningtyas, Sischa
R, Awang Putra Sembada
Pratama, Muhamad Liswansyah

International Journal Of Computer, Network Security and Information System (IJCONSIST)
Vol 7 No 1 (2025): September

This research explores the effectiveness of the Random Forest Classifier method in grouping mental retardation patients based on their level of severity. Medical record data from mental hospitals is collected and processed to train a classification model. The preprocessing process is applied to ensure data quality before use. Model evaluation is carried out by measuring the accuracy of the scores. The research results showed that the Random Forest Classifier succeeded in classifying mental retardation patients with an accuracy of 84%. These findings show the potential of the Random Forest Classifier method as a clinical tool for doctors in determining appropriate interventions for mental retardation patients based on their level of severity.