Perbandingan Algoritma Support Vector Machine dan Random Forest untuk Analisis Sentimen Terhadap Kebijakan Pemerintah Indonesia Terkait Kenaikan Harga BBM Tahun 2022

Samantri, Muhamad
Afiyati

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Vol 8 No 1 (2024): JANUARY-MARCH 2024

The commodity of fuel oil (BBM) is the main commodity and the driving force of business. The increase in world oil prices is a threat to countries around the world, one of which is Indonesia. With the turbulent conditions in several countries, the Indonesian government decided to cut fuel subsidies which had an impact on price increases. The policy invited all Indonesian people and criticized it on various social media. The purpose of this research is to find out which algorithm has a better accuracy rate and to provide input to the government about public opinion regarding the increase in fuel prices in Indonesia. From the test results both work well, this is evidenced by the accuracy value obtained, where the support vector machine algorithm produces an accuracy value of 77%, while the Random Forest algorithm produces an accuracy value of 76%. So it can be concluded that the support vector machine algorithm has a fairly good accuracy rate compared to the Random Forest algorithm.