E. Sugiharti, D. Fauziah
The rise of reuse, reduce, recycle movement impacts both in pollutants management and waste. Including menstrual waste by using the menstrual cup. However, the use of menstrual cups in Indonesia is not yet popular. Sentiment analysis is needed to see how the public thinks about the menstrual cup. Using 1108 data from Twitter, which was then labeled into positive and negative manually, the sentiment analysis stage was carried out using Maximum Entropy and K-Nearest Neighbor. These two were chosen because maximum entropy works by obtaining the best probability distribution, which closest to reality, and k-nearest neighbors work by classifying new objects based on attribute examples and training data. The implementation program uses PyCharm IDE and python programming language. From the research results, maximum entropy and k-nearest neighbors accuracy each are 84.6% and 83.7% with 1108 tweets. In the next research, a lexicon dictionary can be used to replace the manual labeling process. © Published under licence by IOP Publishing Ltd.
Computer Science Department, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang, Indonesia; Computer Science Department, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang, Indonesia
