LEXICON BASED ANALISIS DAN RANDOM FOREST TERHADAP ISU POLITIK DINASTI INDONESIA PADA APLIKASI X - Humuntal Rumapea; Krisna Diva; Harlen Gilbert Simanullang (Universitas Methodist Indonesia, 2026)
Dynastic politics in Indonesia remains a widely discussed issue, eliciting diverse public opinions ranging from support as a political right to criticism of democratic quality, with social media, particularly the X platform, serving as an important venue for public sentiment analysis. This study employs a combination of the Lexicon Based method using the InSet Lexicon and the Random Forest algorithm to analyze public sentiment on dynastic politics. The dataset consists of 1,593 tweets collected from August 1 to December 24, 2024, which underwent text preprocessing, labeling into three sentiment categories: positive, negative, and neutral, and word weighting using TF-IDF. The methodology includes splitting the data into training and testing sets with an 80:20 ratio, applying undersampling on the training data to balance class distribution, and training a Random Forest model with 100 decision trees and a maximum depth of 5 per tree, based on the entropy criterion. Evaluation results show that the model successfully classifies public sentiment with an accuracy of 89%, precision of 82%, recall of 81%, and f1-score of 81%.