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Articles by Retantyo Wardoyo
Total Records ( 2 ) for Retantyo Wardoyo
  Kusrini Last Name , Sri Hartati , Retantyo Wardoyo and Agus Harjoko
  Problem statement: The Case Based Reasoning (CBR) method can be implemented in differential diagnosis analysis. C4.5 algorithm has been commonly used to help the method’s knowledge building process. This process is completed by constructing decision tree from previously handled cases data. The C4.5 algorithm itself can be used with an assumption that all the cases has an exact and equal truth value thus have an exact contribution in decision tree building process. However, the decision makers sometimes not sure about the truth of the cases in the cases database, therefore the confidence value can be different for case by case. Besides that, the C4.5 algorithm can only handle cases that are stored in a flat table with data in form of categorized text or in discrete class. This algorithm has not yet explained about how is decision tree building mechanism in situation when the data are stored in relational tables. It also has not yet explained about the process of knowledge building when the data are in the form of number in continuous class. Meanwhile, the observed objects in this research, that is medical record data, are mostly stored in a complex relational database and have common form of categorized text, discrete number, continuous number and image. Therefore, the C4.5 is needed to be improved so it can handle decision building for cases database of medical record. Approach: We develop a knowledge building framework that can handle confidence level difference of cases in cases database. The framework we build also allows the data are stored in relational database. Moreover, our framework can process data in the form of categorized text, discrete number, continuous number and image. This framework is named CUC-C4.5, abbreviated from Complex Uncertain Case C4.5 as it is the improvement from C4.5 algorithm. Results: The CUC-C4.5 framework has been applied on the case of differential diagnosis knowledge building in a group decision support system to handle geriatric patient. This framework was implemented by using PHP and Javascript programming language and MySQL DBMS. Conclusion: The CUC-C4.5 can support differential diagnosis analysis on group decision support system for geriatric assessment.
  Suwanto Raharjo , Retantyo Wardoyo and Agfianto E. Putra
  Sentence detection also known as sentence boundary detection or sentence boundary disambiguation is one of the study fields in linguistic computation and one of the important stages in the development of an application or research based on natural language processing. Researches topic on Sentence Boundary Detection or Sentence Boundary Disambiguation (SBD) for Indonesian language were not get much intention by researchers as result there are not many paper are written with this topic. The Indonesian language sentence segmentation problems considered as not a big issues and could be using an English SBD method. There are could be the reasons why this topic is not get attention. Existing researches are not specially, discussed on Indonesian language sentence segmentation but only mention as one of stages of research. Two methods, rule based and machine learning are usually used as sentence segmentation methods in several languages. The other methods are using statistic based such as maximum entropy, regression tree or using artificial neural network. This study intended to do sentence segmentation using rule based method on text Indonesian language and comparing the result with existing sentence segmentation softwares. Two models of experiment are conducted on developed rules, first, using input sentences that contain ambiguity problems and second using of many sentences from several kind of input.
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