Browsing by Author "Herlistiono, Iwa Ovyawan"

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    COMPARATIVE STUDY OF THE APPLICATION OF BLENDED LEARNING METHOD VERSUS FACE-TO-FACE LEARNING METHOD IN STUDENT LEARNING ACHIEVEMENT CASE STUDY: Computer Graphic Courses at Widyatama University
    (International Journal of Psychosocial Rehabilitation, Vol.24, Issue 02, 2020) Herlistiono, Iwa Ovyawan; Violina, Sriyani
    The e-learning learning method does not require face-to-face between teachers and students, all teaching and learning activities are bridged by the e-learning system. At the time of implementation there were several obstacles to the implementation of e-learning, including inadequate infrastructure and an independent learning culture that students did not yet have that still required face-to-face activities with the instructor. Blended Learning is a learning method that combines e-learning systems with face-to-face methods. In 1 (one) semester, it is determined when learning is done by e-learning system and when face to face. This study compares student learning outcomes using face-to-face and blended learning methods for Computer Graphic courses at Widyatama University.
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    NAIVE BAYES BINARY CLASSIFICATION FOR FILM REVIEW
    (-, 2023-01) Herlistiono, Iwa Ovyawan; Violina, Sriyani
    Online streaming services provide thousands of movie collections that can be watched by customers, viewers usually choose films based on reviews and ratings. To save time in the selection of good programs, there needs to be a tool to classify the various reviews available to choose which films are worth watching. Classification is an important topic in machine learning and data mining. In this study we use the naïve bayes algorithm which is one of the most efficient and effective algorithms for classification. The data set used in this study is the IMDB film review data taken from [4], as many as 50000 data that has been given 2 types of classification labels, namely "negative" and "positive".It can be concluded that the accuracy of the Naïve Bayes algorithm by applying the Multinomial and Beroulli methods is able to classify film reviews well in all test scenarios with the best accuracy achieved is 84%.