Prediksi Rating Otomatis pada Ulasan Produk Kecantikan dengan Metode Naive Bayes dan N-gram - Irma Pujadayanti; Mochammad Ali Fauzi; Yuita Arum Sari (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
The rise of beauty products also pound Indonesia especially imported products. This has triggered intense competition between local and foreign beauty products industry players. Therefore, the need for innovation in their products. The large number of review data in various online sources is useful as a review material for producers to innovate their products. For Consumer the data is useful as information before buying the product. The review data is often also has not been accompanied by a rating that makes manufacturers have difficulty in categorizing into a certain sentiment. In this study helps to accelerate the categorization of reviews into sentiment in the form of rating. The system built on this research uses the naive bayes classification method and the addition of n-gram method to pre-processing. The use of n-grams including unigram, bigram and combination of unigram and bigram aims to improve the classification results. On testing the best result system in full pre-processing scenario on all n-grams. Accuracy of 50%, 93%, 93% unigram while the accuracy of bigram is 39%, 87%, 83% and the highest accuracy is a combination of 49%, 97%, 96% with tolerance 0, tolerance 1 and sentiment reviews. The results showed that the use of n-grams was enough effective in solving the problems in the study.