Public Sentiment Analysis on TikTok about Tapera Policy using Random Forest Classifier - Muhandhis, Isnaini; Ritonga, Alven Safik (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2025)
At the beginning of 2024, the Tapera policy proposed by the government sparked widespread public debate, resulting in both pros and cons. To improve the quality of public services, it is crucial for the government to evaluate policies to align with the needs and expectations of the community. This study aims to analyze public sentiment on the social media platform TikTok regarding the Tapera policy. Comment data was collected from several TikTok videos discussing the Tapera policy with high view counts. These videos received various responses in the form of comments, expressing positive, neutral, and negative sentiments about Tapera. A total of 5,036 comments were successfully scraped. The Random Forest Classifier was used for sentiment classification. This method was chosen for its ability to maintain high predictive accuracy, minimize overfitting, and perform effectively in classification tasks. The study results showed that negative sentiment dominated TikTok users' opinions, accounting for 82%, followed by neutral sentiment at 10% and positive sentiment at 8%. Many expressed disapproval for various reasons, including concerns about potential corruption, the ineffectiveness of contributions due to inflation, and the policy being burdensome amid a sluggish economy. Neutral sentiment was dominated by questions related to Tapera, such as the amount of Tapera deductions and whether participation is mandatory for those who already own a house. Positive sentiments expressed support for the Tapera policy and willingness to pay the contributions. However, the proportion of supporters of this program was significantly smaller than those opposing it. The training results of the classification model using the Random Forest Classifier achieved an accuracy of 89%. The highest F1-score for detecting negative sentiment was 94%, while the F1-score for detecting neutral sentiment was 17% and for positive sentiment, it was 32%. This disparity is due to the dataset composition being dominated by negative sentiment. The proportion of sentiment significantly influences the training of the classification model. A balanced proportion for each sentiment would enable the model to better learn and recognize the words frequently associated with each sentiment.