Comparison of Sentiment Analysis on Digital Platform Application Reviews Using Naive Bayes and Support Vector Machine

Solihin, Abdu Muhammad (2024) Comparison of Sentiment Analysis on Digital Platform Application Reviews Using Naive Bayes and Support Vector Machine. Skripsi thesis, Universitas Pakuan.

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Abstract

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

Item Type: Thesis (Skripsi)
Subjects: Fakultas Ilmu Pengetahuan Alam dan Matematika > Ilmu Komputer
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam > Ilmu Komputer
Depositing User: PERPUSTAKAAN FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNPAK
Date Deposited: 19 May 2025 03:04
Last Modified: 19 May 2025 03:04
URI: http://eprints.unpak.ac.id/id/eprint/9584

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