ANALISIS SENTIMEN PENGGUNA TIKTOK TENTANG PROGRES PEMBANGUNAN IKN DENGAN METODE RANDOM FOREST - Siti Rihastuti; Afnan Rosyidi (Journal of Computer Science and Technology (JCS-TECH), 2025)
Abstract Sentiment classification is a text analysis technique used to identify and categorize user opinions about an application or service. This study aims to classify public sentiment about the progress of the development of the IKN (Indonesian Capital) with the Random Forest algorithm based on comments from users of the Tiktok platform. The dataset was taken from Kaggle with 1472 comments in Indonesian. The dataset used consists of user comments categorized into positive and negative sentiments. The evaluation was carried out based on the accuracy, precision, recall, and F1-score metrics to determine the results of the user sentiment classification. Testing the Random Forest method on Google Colab showed an accuracy value of 77%, precision 78%, recall 77% and F1-score 77%. From these values, the Random Forest method is considered quite good in classifying Tiktok user sentiment in responding to the progress of the IKN relocation.