OPTIMASI RANDOM FOREST TERHADAP DATA PENYAKIT LIVER MENGGUNAKAN FIREFLYALGORITHM

Sunarjo, Nemesius
Nugraha, Danang Aditya
Santoso, Heri

BIMASAKTI
Vol 8 No 2 (2026): BIMASAKTI

Liver disease is one of the most dangerous diseases for human survival. In an effort to find out liver disease early on, a classification method is needed. Researchers conducted testing and classification of lliver disease with the Random Forest algorithm which was then optimized with the Firefly algorithm. The purpose of this study is to learn how the application of the firefly algorithm in optimizing the accuracy of the random forest algorithm in liver disease. The data used is 1700 data with 11 attributes. The findings of this study with the Random Forest algorithm produced an accuracy of 87.24% while when optimized using the Firefly Algorithm produced an accuracy of 93.24%. The findings demonstrated a rise in the precision of the Random Forest algorithm and optimized using Firefly Algorithm.