Decision Making Model to Identify Recommended Topics Based on Sentiment Analysis: The Case Blockchain Indonesia

Decision making model to identify recommended topics based on sentiment analysis contains 6 main stages, namely Web Scraping, Preprocessing Data, Clustering Analysis, Sentiment Analysis, Decision Making and To Identify Recommended Topics. In a previous study, decision making requires several alternative decisions that are ready to be chosen. This paper proposes to add another stage to the previous study so that decision making can be done without several alternative decisions that are ready to be chosen. Several alternatives are obtained from clustering analysis. The results of this paper aim to identify recommended topics from a discussion that the paper discusses blockchain. Wordcloud analysis is used to identify recommended topics. The findings revealed that discussions largely centered around economic and industrial issues, as well as regulatory and legal issues. The resulting recommendations cover topics regarding upgrading blockchain platforms to enhance economic progress and market expansion, in addition to considerations regarding the implementation of more transparent regulations to streamline the integration of blockchain technology in Indonesia. This investigation provides valuable insights for policymakers, industry professionals, and scholars looking to optimize the efficacy of blockchain technology in Indonesia.