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The government that is running at this time is also not spared from public comments on Twitter, especially regarding the increase in subsidized fuel. There are at least 4 impacts felt by the community when subsidized fuel prices increase, namely a decrease in people's purchasing power, an increase in basic prices, an increase in the unemployment rate and an increase in the poverty rate. This study aims to implement the Naïve Bayes Classifier and KNN algorithms in classifying a tweet of an increase in subsidized fuel so that it can be identified as belonging to a class with positive or negative sentiments. The data used in this research are 560 tweets. The data is divided into 2, namely 500 training data from tweet data and 60 test data from tweet data stored in xlsx format. The results of the accuracy with the Naïve Bayes Classifier algorithm is 85% while the KN algorithm is 86.8% so it can be concluded that the KNN method is better than the Naïve Bayes Classifier method in classifying tweets of increases in subsidized fuel. Keywords: Subsidized BBM, Naive Bayes, KNN

Agriculture is an activity of managing biological natural resources with the help of technology, capital, labor and management to produce agricultural commodities which include food crops, horticulture, plantations and or livestock in an agro-ecosystem. Most of the Indonesian people's livelihoods are as farmers, so the agricultural sector is very important to develop in this country. Poktan Beno Raharjo in Glonggong Village, Madiun Regency has a problem, namely the absence of an information system that is useful for managing agricultural production data, managing farmer data, managing village granary data, recording planting of food crops and horticultural crops, managing subsidized fertilizer assistance. The mobile-based Poktan information system was created using the dart programming language with the flutter framework using the waterfall method and testing using the black box testing method.