Information can also be a means of learning for humans. Including information about history because history can be a means of learning for the younger generation to appreciate the nation's culture and build national identity. In the past, the Sumedang Larang kingdom was one of the many kingdoms in West Java, Indonesia, that could be used as much information as a lesson. Technological developments make more and more information available for study. We need the proper means to find the information we need. This study aims to build a Question Answering (QA) system to create a means for the younger generation to be more familiar with the history of the kingdom in the past. The QA system offers an information retrieval system that is easy to access and can immediately provide the answers we need. This QA system was built using ontology as a knowledge base and cosine similarity to determine the similarity between user questions and the dataset. The QA system that has been built is tested by providing a set of questions so that the system's performance can be measured, and the results of system testing get a precision value of 70% and a recall value of 90%.

In the news portal text, there is a lot of important information such as the name of the person, the name of the organization, or the name of the place. To obtain information in text documents manually, humans must read the contents of the entire news text. To overcome this issue, information extraction, commonly called Named Entity Recognition (NER) was used. The extraction of information expressly for the NER field is used to make it easier to process word or sentence data. It helps search engines and also helps to classify places, times, and organizations. There is a limited number of NER in Indonesian texts using only the Recurrent Neural Network (RNN) method. Similar previous studies only employed other versions of RNN such as LSTM (Long Short Term Memory), BiLSTM (Bidirectional Long Short Term Memory), and CNN (Convolutional Neural Network). NER is one of the answers to the problems that exist in a large number of news portal texts to obtain information effectively and efficiently. The results of this study indicate that the NER system using the RNN method in Indonesian news texts has an F1 -Score of 80%

News is a medium of daily information usually obtained by the public. The news consists of a lot of information in it and is composed of sentence structures. Each language is unique with its own sentence structure, like Indonesian and other foreign languages. But nowadays, many media mix Indonesian with foreign languages, making the sentence structure different from Bahasa Indonesia. To classify these words, Part Of Speech Tagging needed to determine the class of words composed of sentences by learning from the Corpus of each language. With the new sentence structure, POS Tagger requires a larger Corpus to learn. The language structure can determine the results of tagging from the POS Tagger. If there are words that are not in the Corpus, it can reduce the accuracy of the POS Tagger. We conducted to enhance the research results by adding data with a different sentence structure from the Indonesian Language Corpus using sentences from online media. Added about 242 sentences with 7,043 tokens on Corpus focused on Foreign Word tags, which total 3819 tags. After doing some testing and scenarios, the results of the accuracy of POS Tagger show an accuracy of 94.7% using the Hidden Markov Model method with the F1-Score tag FW 78%.

In the current digital era, the development of information technology is growing rapidly. The development of information technology is followed by the development of social media, one of the social media that is on the rise is Twitter. Because there are many Twitter users around the world, Twitter stores a lot of data that can be used for something, one of which is to determine the category of public opinion about a company or university, in this study the focus is more on the category of public opinion about Telkom University. The public opinion can be grouped or categorized to make it easier to determine the topic being discussed. Determining opinions manually will take a long time due to the large number of tweets. Therefore, there must be another method to determine the categories of public opinion on Twitter. One of them is the Latent Dirichlet Allocation (LDA) method with a dataset of tweets of Indonesian-language Twitter users. With this method, grouping tweets on a large scale is more efficient. From the modeling made, the most optimum results obtained with a coherence score using the c_umass method of -15.33029 with a combination of 9 topics, 0.31 alpha value, and 0.01 beta value.

Sentiment analysis is an analysis in terms of opinion and meaning in the form of writing. Sentiment analysis is very useful for expressing opinions from any individual or group to improve branding.  Branding is a process to promote and improve the name of a brand or brands to attract the attention of consumers to be interested in trying the services of a company that runs in academic terms such as Telkom University. However, this requires cooperation between other associations as partners so that the branding carried out can be effective. One form of cooperation is by providing opinions about Telkom University so that consumers are more familiar with Telkom University on Twitter social media which is the largest social media used by many people because it can provide any opinion freely. Therefore, this study aims to analyze the sentiment submitted by partners for Telkom University on Twitter which is the main factor for promoting themselves to consumers. The process carried out is to take all tweets about Telkom University submitted by partners and then carry out the TF-IDF weighting process and classified using the Decision Tree CART algorithm based on positive, negative, and neutral sentiment categories. The best results obtained by the Decision Tree model of the CART algorithm are the Accuracy value of 86.73%, Precision of 87.06%, Recall of 87.55%, and F1-Score of 86.52%.