%0 Thesis
%9 Skripsi
%A Solihin, Abdu Muhammad
%A Universitas Pakuan,
%A Fakultas Matematika dan Ilmu Pnegetahuan Alam,
%A Program Studi Ilmu Komputer,
%B Fakultas Matematika dan Pengetahuan Alam
%D 2024
%F eprintsunpak:9584
%I Universitas Pakuan
%P 20
%T Comparison of Sentiment Analysis on Digital Platform  Application Reviews Using Naive Bayes  and Support Vector Machine
%U http://eprints.unpak.ac.id/9584/
%X Comparison of Sentiment Analysis on Digital Platform  Application Reviews Using Naive Bayes  and Support Vector Machine  Abdu Muhammad Solihin1*  , Tjut Awaliyah Zuraiyah 2  , Dini Suhartini 3  1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan  University, Bogor, West Java, 16143, Indonesia  Abstract  The development of digital technology has changed how users interact with various services through  applications. User reviews on digital platform applications have become an important source of  information for understanding user sentiment and experience. This research analyzes user sentiment  towards digital platform applications and compares the performance of the Naive Bayes and Support  Vector Machine (SVM) algorithms. The data used is user reviews of the TipTip application from the  Google Play Store and App Store. The research methodology includes preprocessing, Term FrequencyInverse Document Frequency (TF-IDF) weighting, and classification using Naive Bayes and SVM with  Linear, RBF, Polynomial, and Sigmoid kernels. To address data imbalance, the Synthetic Minority  Oversampling Technique (SMOTE) is used. The results show that SVM with the RBF kernel achieves  the highest accuracy (98%), while Naive Bayes reaches 90%. The analysis also reveals a general trend of  positive user sentiment towards the application. This study demonstrates the effectiveness of SVM,  especially with the RBF kernel, in sentiment analysis of application reviews and highlights the impact of  SMOTE in handling data imbalance.  Keywords: Sentiment Analysis; Naive Bayes; Support Vector Machine; SMOTE; Application Reviews