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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Fathor Rosi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Prediksi Rating Pada Review Produk Kecantikan Menggunakan Metode Naive Bayes Dan Categorical Proportional Difference (CPD) Fathor Rosi; Mochammad Ali Fauzi; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Beauty products at this time become a popular thing in various circles, especially among women. Almost all of them have beauty products and are included as a primary requirement to support their better performances. The existence of a product can not be separated from a comment or review of the consumer for the product. Of course with the review can help consumers to be more selective again in choosing a product. And from the production side can be helped to measure how far the quality of the products they produce. But from the production itself sometimes have difficulty in sorting and categorize the review, whether the product is good quality, good enough, not good, and so forth. In this study the assessment of a product 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 product. To support the system built required methods to solve the problem, in this study researchers used the method of Naive Bayes and Categorical Proportional Difference. Naive Bayes is a method for classification whereas Categorical Proportional Difference is a feature selection to further optimize the results of classification. From the test results, obtained the best accuracy level when the use of features by 50% with an accuracy of 87%. These results are the best results of the results with other feature usage ratios of 25%, 75% and 100%. From these results CPD proven to make the selection of words that are considered relevant or irrelevant to do classification.
Co-Authors M. Ali Fauzi Rizal Setya Perdana