Abstract

Twitter is the most popular microblogging service in Indonesia, with nearly 23 million users. In the era of big data such as the current tweets from customers, observers, potential consumers, or the community of users of products or services of a company will greatly help companies in knowing the industrial and consumer landscape, so as to determine strategic plans that will contribute to the company's growth. However, the use of data from social media such as Twitter is hampered by a number of technical difficulties in the process of collecting, processing, and analysing. Specifically, this research applies the Naïve Bayes Classifier algorithm in the process of sentiment analysis of tweets data into a prototype application that is intended to make it easier for companies / organizations to know people's perceptions of their products or services. The NBC algorithm was chosen because this algorithm is able to do a good classification even though it uses small training data, but has high accuracy and process speed for handling large training data. From the evaluation results found a prototype running well where the keywords entered will trigger the Twitter API to crawl the data then the mining process can be monitored at each stage and at the end of the process, the system will show the final sentiment level values and the representation of the calculation results log in a chart form over a certain period of time.