Analisis Sentimen Data Twitter Tentang Ekonomi Sirkular Menggunakan Algoritma Neural Network Berbasis Particle Swarm Optimization

  • Fatihanursari Dikananda STMIK LIKMI Bandung
  • Ahmad Rifa'i STMIK LIKMI Bandung
  • Gifthera Dwilestari STMIK IKMI Cirebon
Keywords: Ekonomi sirkular, Senitmen Analisis, Neural Network, Particel Swarm Optimizer


The circular economy is a renewable and resilient industrial system that "eliminates" the end product cycle by implementing new procedures and business models, using renewable energy and chemicals. Develop use-based products for waste elimination and minimization. Some activities in the circular economy in people's lives include innovative solutions in plastic waste management, supply chain management, and charcoal briquettes from dry leaves. One application of the principles of a circular economy, such as waste management, is to classify, manage and develop plastic waste into a circular economy of valuable plastic waste. This means that it can support the economic life of the community. This study aimed to find out public opinion regarding the circular economy, which was conveyed through social media Twitter. Based on the reviews and public opinion about the circular economy that was shared through the Twitter media, sentiment analysis was conducted by classifying these opinions into positive, negative, and neutral reviews. The method used in this research is a machine learning technique with a neural network algorithm based on particle swarm optimization (PSO). The results of this research on Twitter data sentiment analysis on circular economy obtained a population size of 4 for particle swarm optimization parameters so that the accuracy rate reaches 75%. Using the neural network+PSO algorithm, while using the neural network algorithm alone, it gets an accuracy rate of 71.67%.