PERBANDINGAN NLP DAN SVM DALAM ANALISIS SENTIMEN KOMENTAR INSTAGRAM TERKAIT STIGMA MASYARAKAT TERHADAP BANJIR SUMATERA 2025 - Muhammad Akbar Firdaus; Maisya Fitri Anugrah; Sri Hidayati; Dedy Rahman Harahap; Rendy Rabensi Sembiring; Muhammad Syahputra Novelan (JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 2026)
This study aims to analyze public sentiment regarding the 2025 Sumatra flood based on Instagram comments using a Natural Language Processing (NLP) approach. The methods applied include IndoBERT and Support Vector Machine (SVM) with TF-IDF features. A total of 711 comments were collected through a crawling process and processed using preprocessing techniques. The results show that negative sentiment dominates at 44.7%, followed by positive (30.4%) and neutral (24.9%) sentiments. Model evaluation indicates that IndoBERT outperforms SVM with an accuracy of 74.8% compared to 66.4%. WordCloud visualization reveals dominant terms such as flood, government, forest, and palm oil, reflecting public concerns about environmental issues and government policies.