This research focuses on developing a method to analyze why-questions.  Some previous researches on the why-question analysis usually used the morphological and the syntactical approach without considering the expected answer types. Moreover, they rarely involved domain ontology to capture the semantic or conceptualization of the content. Consequently, some semantic mismatches occurred and then resulting not appropriate answers. The proposed method considers the expected answer types and involves domain ontology. It adapts the simple, the bag-of-words like model, by using semantic entities (i.e., concepts/entities and relations) instead of words to represent a query. The proposed method expands the question by adding the additional semantic entities got by executing the constructed SPARQL query of the why-question over the domain ontology. The major contribution of this research is in developing an ontology-based why-question analysis method by considering the expected answer types. Some experiments have been conducted to evaluate each phase of the proposed method. The results show good performance for all performance measures used (i.e., precision, recall, undergeneration, and overgeneration). Furthermore, comparison against two baseline methods, the keyword-based ones (i.e., the term-based and the phrase-based method), shows that the proposed method obtained better performance results in terms of MRR and P@10 values.

Movies are an entertainment that is in great demand by many groups from children, teenagers, adults, and parents. In the current digital era, various films can be watched on television to digital streaming services. Public opinion on the films watched can be in the form of positive opinions or negative opinions. Sentiment analysis is one of the fields of Natural Language Processing (NLP) which is able to build a system to recognize and extract opinions in the form of text, sentiment analysis is usually used to find out people's opinions or assessments of a products, services, politics, or other topics. Through sentiment analysis from the collection of reviews, the public can get various recommendations for films that can be watched. The method implemented to classify review data into positive reviews and negative reviews in this study is LSTM by comparing two different optimizers, namely Adam and RMSprop. This study succeeded in providing sentiment predictions with different optimizers with accuracy values ??for the LSTM application with Adam Optimizer reaching 77.11% and the LSTM application with RMSprop reaching 80.07%.

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%

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