Implementation of IndoBERT in Sarcasm Detection using Random Forest Towards Sentiment Analysis - Sibarani, Sabrina Adela Br; Purba, Ronsen; Limbong, Ricky Paian (Forum Kerjasama Pendidikan Tinggi, 2025)
Sarcasm, a subtle form of irony, often introduces a discrepancy between the literal meaning of words and the intended message, making it a significant challenge for sentiment analysis systems. Misinterpreting sarcasm in social media comments can lead to inaccurate sentiment classification, hindering decision-making processes in areas like customer feedback analysis and social opinion mining. This study addresses this issue by evaluating the effectiveness of sarcasm detection in Indonesian text using a Random Forest Classifier (RFC) integrated with IndoBERT. The research employs 10-fold cross-validation to measure performance. Without IndoBERT, the RFC model achieved average accuracy, precision, recall, and F1-score of 78.83%, 78.83%, 79.01%, and 78.83%, respectively. Incorporating IndoBERT significantly improved performance, with all metrics exceeding 84%. Furthermore, 5-fold cross-validation achieved the highest performance, with all metrics reaching 97.24%. This research contributes to developing more robust natural language processing models tailored to Indonesian linguistic contexts, specifically for sarcasm detection.