M. L. Khodra

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Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel classification for Indonesian news articles. Since this classification deals with a large number of features, we also employ some feature(More)
The rapid growth of social media, especially Twitter in Indonesia, has produced a large amount of user generated texts in the form of tweets. Since Twitter only provides the name and location of its users, we develop a classification system that predicts latent attributes of Twitter user based on his tweets. Latent attribute is an attribute that is not(More)
Electrical energy plays an important role in Indonesia's economic development. In Indonesia, many islands other than Java, Bali, Madura, still depend on Diesel Engine as the source of electrical energy. Biodiesel as an alternative of diesel engine also makes diesel engine as one possible solution for producing electrical energy. In order to be able to use(More)
Time constraints often lead a reader of scientific paper to read only the title and abstract of the paper, but reading these parts is often ineffective. This study aims to extract information automatically in order to help the readers get structured information from a scientific paper. The information extraction is done by rhetorical classification of each(More)
Learning to rank is a technique in machine learning for ranking problem. This paper aims to investigate this technique to classify the responsible agencies of each complaint text of LAPOR, which is our government complaint management system. Since this categorization problem is multilabel one and the latest work using learning to rank for multilabel(More)
Aspect extraction is an important task in sentiment analysis to identify aspects in customer review products. Most existing works defines the pattern set manually or using heuristic approach. In this paper, we propose SAE, a Syntactical-based Aspect Extraction using decision tree and rule learning to generate the pattern set based on sequence labelling. We(More)
Aspect extraction is an important step in opinion mining to identify aspect in customer review products. Most existing works defines the pattern set manually or using heuristic approach. In this paper, we propose learning-based approach using decision tree and rule learning to generate pattern set based on sequence labelling. The patterns will be used to(More)
In various real case, imbalanced datasets problems are inevitable, such as in metal detecting security or diagnosis of disease. With the limitations of existing learning algorithms when faced with imbalanced datasets, the prediction error is caused by the dominance of the majority against the minority class. Various techniques have been made to address the(More)
There are two problems in using words to represent document contents and query in information retrieval: ambiguity and different words which represent the same concept. These problems can be addressed by using query expansion. We focused on analysing the implementation of query expansion, word sense disambiguation (WSD), iterated relevance feedback, and(More)
Information extraction is a process to find structured text from unstructured or semi-structured text. This research has an objective to build an information extraction system specialized for Events in Indonesian tweets. The system consists of two main parts. First part filters relevant tweet from irrelevant tweet. This part is only using a rule based(More)