Optimization Based Random Forest Algorithm Modification for Detecting Monkeypox Disease
Rinci Kembang Hapsari, Endah Purwanti, Wahyu Widyanto, Ricky Gunawan, Firdausiyah Nurlaily, Abdullah Harits Salim · 2023
Monkeypox is a zoonotic infectious disease caused by orthopoxvirus. Common symptoms that could indicate Monkeypox are fever, headache, muscle aches, back pain, tiredness or unwell, and swollen lymph nodes. In this research, we conducted a predictive study on Monkey Pox using the Random Forest algorithm optimized with Particle Swam Optimization, which we call PRFO. This algorithm can improve the performance of ordinary Random Forests by making a relatively fast running time and increasing the accuracy value of the algorithm. The results of tests that have been carried out on three datasets, namely: 1) The MonkeyPox dataset, which has 25 thousand data, shows an increase in the accuracy value of 2.08% from 67.80% to 69.88%, 2) The Health dataset with 20 thousand data increased by 0.89% from 92.79% to 93.67%, and 3) The PulsarStar dataset with 12 thousand data increased by 0.27%, From 97.89% to 98.16%. The increase in value is based on the PRFO parameter, which only uses 30 particles with a maximum of 50 iterations. From the tests that have been carried out, the application of the PRFO algorithm can find the best solution on a dataset with more than 10 thousand data in a relatively short time.