<![CDATA[Social media is a platform for people to sharing their views on certain things or events. One of the things discussed on social media is the Job Creation Law (UU Cipta Kerja). The Job Creation Law is a law that simplifies regulations related to licensing activities in starting a business and investing with the aim of increasing Indonesia's economic growth. Twitter is a social media where many users share their opinions regarding the Job Creation Law. Therefore, Twitter can be a source for analyzing user opinions regarding the Job Creation Law. This research was conducted to analyze the sentiment on tweets (a term for content uploaded to Twitter) regarding the Job Creation Law by classifying tweets into two categories, positive and negative sentiment. The classification process is done by implementing an Artificial Neural Network (ANN) trained by Backpropagation algorithm and term weighting using Term Frequency-Inverse Document Frequency (TF-IDF) method into Python. There are several stages in this study, there are data scrapping, data labeling, preprocessing, term weighting, training, testing, and performance evaluation. The best performance obtained by the ANN is 95% accuracy, 98% precision, 92.4% recall, and 95.1% f-measure. This value was obtained when the ANN trained with 1500 epochs, 1 hidden layer with 50 hidden nodes, and 0.2 learning rate]]>
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