ANALISIS SENTIMEN POSITIF APLIKASI GUITARTUNA DAN FENDER GUITAR TUNER DENGAN MENGGUNAKAN METODE ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE - Arkiza Ariq (2024)
In the current era of digital transformation, there are many applications that people often use, one of which is music applications. One of these music applications is the GuitarTuna application which is used to tune guitars. Many users have experienced complaints about the application, starting from features that charge users suddenly, tuners that are less accurate, several features that are locked for non-premium users, etc. Therefore, the author wants to conduct research to compare the GuitarTuna application with a similar application, namely Fender Guitar Tuner, to provide recommendations for future developers of similar applications. The dataset was obtained from Google Play Store with a total of 1000 datasets and was not yet labeled. In data classification, it is necessary to label the data and preprocess the data before entering the text processing stage, then the data will be given weight to each word using the TF-IDF (Term Frequency - Inverse Document Frequency) method. The solution provided is to carry out sentiment analysis using Naïve Bayes and Support Vector Machine algorithm methods by dividing the data into training data and test data by 80% and 20%. The results obtained from this research were an accuracy value of 92% in both applications using the Naïve Bayes method and accuracy values of 97% and 95% in both applications using the SVM method. Based on the accuracy values of the two applications, the SVM algorithm method has a superior value compared to the Naïve Bayes algorithm method.