Sentiment Analysis on Public Perception of the Nusantara Capital on Social Media X Using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) Methods - Haliza, Dinda; Ikhsan, Muhammad (Journal of Applied Informatics and Computing, 2025)
The relocation and development of the National Capital City (IKN) as the center of government activities has become a hot topic, sparking diverse opinions among the public. The proposal to move the capital from DKI Jakarta to East Kalimantan has drawn significant attention from online communities, particularly on social media platform X (Twitter). This study aims to explore public sentiment regarding the development of IKN by applying artificial intelligence-based classification algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). Sentiments are categorized as positive or negative to provide deeper insights into public perceptions. Through web crawling techniques, a total of 4,000 data points were collected. After the preprocessing stage, 3,608 data points remained, which were then translated into English to facilitate labeling using the Vader Sentiment method. The analysis results indicate that negative sentiment (1,873) is more dominant than positive sentiment (1,735). The data was then split into two sets: 80% for training (2,886 data points) and 20% for testing (722 data points). Based on the evaluation results, SVM and K-NN proved to be effective for sentiment analysis. SVM achieved an accuracy of 76%, precision of 78%, recall of 81%, and an f1-score of 79%, while K-NN attained an accuracy of 65%, precision of 62%, recall of 98%, and an f1-score of 76%. With superior performance, SVM emerges as a more reliable method for classifying public sentiment regarding the IKN development policy.