Sentiment Analysis Of Public Opinion On Handling Stunting In Indonesia Using Random Forest

The problem of stunting is important to solve, as it has the potential to disrupt human resource potential and is linked to health outcomes and even child mortality. The Indonesian government targets the stunting rate to drop to 14 percent by 2024 through an accelerated stunting reduction program as an effort to improve the nutritional status of the community and also reduce the prevalence of stunting or short toddlers. Understanding public sentiment towards stunting initiatives is essential for policy makers and stakeholders to design effective interventions and allocate resources efficiently. In this research, classification of positive and negative sentiment is carried out using the random forest algorithm. The data used is comment data on one of the social media pages, namely Twitter, regarding public sentiment towards handling stunting cases in Indonesia. The first stage in this research after obtaining a data is data preprocessing. The data preprocessing stage in sentiment analysis is useful for cleaning and normalizing text, removing irrelevant words, and preparing data so that algorithms can analyze sentiment more accurately and efficiently. Furthermore, the results of the preprocessed data are labeled 0 for positive and 1 for negative labels. The classification of positive and negative sentiment was done using random forest and resulted in an accuracy value of 97.5%. This model is good, but we suggest trying other algorithms in future research.