BibTex Citation Data :
@article{J.Gauss39889, author = {Laurentina Adinda Puspita Sari and mustafid mustafid and Triastuti Wuryandari}, title = {PENERAPAN METODE MULTINOMIAL NAÏVE BAYES DENGAN SELEKSI FITUR INFORMATION GAIN UNTUK ANALISIS SENTIMEN TERHADAP LAYANAN INDIHOME}, journal = {Jurnal Gaussian}, volume = {14}, number = {1}, year = {2025}, keywords = {MyIndihome; Sentiment Analysis; Multinomial Naïve Bayes; Information Gain}, abstract = { MyIndihome is one of PT Telkom Indonesia's innovations in the form of an application to provide the best service to customers regarding indihome products. Indihome users who continue to increase make PT Telkom must be ready to face complaints that are usually channeled through social media, such as on the Google Play site of the MyIndihome application. Sentiment analysis is needed to determine the classification of customer reviews through the MyIndihome application which is carried out using the Multinomial Naïve Bayes method. The application of this method was developed by selecting information gain features to obtain relevant features. The Multinomial Naïve Bayes method relies on strong independence assumptions and is straightforward to implement for text classification. This method considers both the presence and frequency of words. Performance evaluation uses a confusion matrix, revealing that the Multinomial Naïve Bayes method achieves 93% accuracy without feature selection and 95% with information gain feature selection. This indicates that incorporating information gain can enhance the classification accuracy of MyIndihome customer reviews. }, issn = {2339-2541}, pages = {54--61} doi = {10.14710/j.gauss.14.1.54-61}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/39889} }
Refworks Citation Data :
MyIndihome is one of PT Telkom Indonesia's innovations in the form of an application to provide the best service to customers regarding indihome products. Indihome users who continue to increase make PT Telkom must be ready to face complaints that are usually channeled through social media, such as on the Google Play site of the MyIndihome application. Sentiment analysis is needed to determine the classification of customer reviews through the MyIndihome application which is carried out using the Multinomial Naïve Bayes method. The application of this method was developed by selecting information gain features to obtain relevant features. The Multinomial Naïve Bayes method relies on strong independence assumptions and is straightforward to implement for text classification. This method considers both the presence and frequency of words. Performance evaluation uses a confusion matrix, revealing that the Multinomial Naïve Bayes method achieves 93% accuracy without feature selection and 95% with information gain feature selection. This indicates that incorporating information gain can enhance the classification accuracy of MyIndihome customer reviews.
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