ANALISIS SENTIMEN WARGANET TWITTER TERHADAP PROVIDER BY.U MENGGUNAKAN METODE NAIVE BAYES CLASSIFICATION

MANAARUL HIDAYAT, NIM. 16650071 (2021) ANALISIS SENTIMEN WARGANET TWITTER TERHADAP PROVIDER BY.U MENGGUNAKAN METODE NAIVE BAYES CLASSIFICATION. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

With the rapid growth of the number of youth, several providers have launched digital service provider innovations. By.U as a pioneer has been serving this segment for the past year and its presence has received various responses from netizens. This response, if researched, can improve services and lead to other innovations. However, there is no research that addresses this. Therefore, a final project was written regarding Sentiment Analysis on provider by.U. In this final project, a classification model was made with 3804 data using Naive Bayes Classifier and TF-IDF with Bi-Grams. Comparison was made by eliminating one of the preprocessing steps: stopword removal. From the comparison, it was found that TF-IDF and Bi-Grams without the application of stopword removal had the best performance values. The best performance value is obtained in the split validation scenario 90:10 with accuracy 86.88%, Precision 88.24%, recall 83.33% and f1-score 85.71%. The model is used to classify 1075 new data from 20-27 December 2020 with a positive sentiment prediction of 54.9% and a negative sentiment of 45.1%. Words that often appear on positive sentiment include “25gb”, “package”, “promo”, “buy”, “quota”, “cheap”. Words that often appear in negative sentiment include “signal”, “slow”, “bad”, “admin”, “net”. There were 842 tweets from Android, 157 of them came from Iphone.

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