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Nowadays social media has become one of the tools to express idea or opinion. They are more active expressing it on social media instead of speaking directly. Twitter is the most popular among them to express idea, also share news, picture, music and etc. Twitter users are increasing significantly each year as the result the information grows in same way. Due too much information flow, people get difficulties to make sure or clarify the news. For example, Looking for the information about a figure who will participate in a Pilkada. There are many researchers analyze subjectively and haven’t given the maximum result yet. This research is trying to clarify information and divided them into positive, negative and neutral information. It is using TF algorithm and Neutral Network as the tools. The dataset is taken from a figure’ twitter which is participate in Pilkada. And the result shows that accuracy 66.92%, positive precision 67.80%, negative precision 64.29%, neutral precision 73.33%, and positive recall 80%, negative recall 70%, neutral recall 36.67%.

Now finding relevant information is absolutely needed by users of the information. Actually, finding relevant information is more difficult than just looking for information. The presence information retrieval (IR) system is a system for automatically searching the relevant information to the user needs, which is expressed in the required query, and this query will be used as an input and processed by IR system and then will get relevant documents to the required query. One of methods of finding relevant information by query is term weighting by both query and document. The commonly used methods are the term weighting of local and global weighting, while the local term weighting algorithms that are used such as TF, TF logarithmic, binary TF and augmented TF and global weighting is IDF, IDFP, IDFB. In this research, besides of comparison of term weighting algorithms, it is also proposed new improvement in term weighting algorithms by adding global weight value that changes base 10 logarithmic to base 2 logarithmic and adds the number 1 in the global term weighting IDFP. Measurements result of this research are Precision, Recall and NIAP. In This research, the best result for precision and NAIP have been performed by new propose term weighting algorithms. After compare and analyze the result of the weighting algorithms. It is necessary to combine the weighting with semantic algorithms so that it will get better result.