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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Thio Marta Elisa Yuridis Butar Butar
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Penentuan Rating Review Film Menggunakan Metode Multinomial Naive Bayes Classifier dengan Feature Selection Berbasis Chi-Square dan Galavotti-Sebastiani-Simi Coefficient Thio Marta Elisa Yuridis Butar Butar; Mochammad Ali Fauzi; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the current era there are various kinds of movies, although the way of approach varies, all movies can be said to have one goal, namely to attract people's attention to the contents of the problem. From the contents of the movie there are many responses from the author and write them in a short review. With the review can help consumers to be more selective again in choosing a movie. And from the production side can be helped to measure how far the quality of the movies they produce. But from the production itself sometimes have difficulty in sorting and categorize the review, whether the movie is good quality, good enough, not good, and so forth. In this study the assessment of a moview based on the review given is Rating. So it takes a Rating prediction system to predict and determine the right Rating based on the reviews given by the users of a movie. To support the system built required methods to solve the problem, in this study researchers used the method of Multinomial Naive Bayes along Chi-Square and Galavotti-Sebastiani-Simi Coefficient. Multinomial Naive Bayes is a method for classification whereas Chi-Square and Galavotti-Sebastiani-Simi Coefficient is a feture selection to futher optimize the results of classification. From the test results, obtained the best accuracy level when the use features by 90%, and 100% with an accuracy of 36%. These results are the best results of the results with other features usage percentages. From these results CHI-GSS proven to make the selection of words that are considered relevant or irrelevant to do classification.
Co-Authors Indriati Indriati M. Ali Fauzi