Analysis of Public Perception on Organic Coffee through Text Mining Approach using Naïve Bayes Classifier

Abstract

Productivity of organic coffee plants in Indonesia is still lower if compared by productivity of coffee which use ordinary cultivation. One of the problems, which is faced by farmers to develop organic coffee is no certainty market. This could be because not all people of Indonesia are able to buy organic coffee products which quite expensive. Based on these, it is necessary to analyze public perception sentiment of organic coffee products, to identify potential and opportunities the development of organic coffee farming in Indonesia. This research uses a text mining approach to classify the public perception sentiment on organic coffee products based on tweet which posted in social media, i.e., twitter. Sentiment classification is performed by Naïve Bayes Classifier algorithm. The most of sentiment value formed in this research is positive sentiment. These results show that the public perception on organic coffee is in positive manner. So that the prospect of organic coffee plants development in Indonesia and the market opportunity of organic coffee products are predicted to rise as well.