ANALISIS SENTIMEN ULASAN APLIKASI MYXL PADA GOOGLE PLAY STORE MENGGUNAKAN NAIVE BAYES

MAHENDRA, AZZA AZRA and Novia, Novia and Rodiah, Desty (2025) ANALISIS SENTIMEN ULASAN APLIKASI MYXL PADA GOOGLE PLAY STORE MENGGUNAKAN NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

The growth of internet users in Indonesia, reaching 221 million in 2024, has significantly impacted the digital sector, including operator service applications such as myXL. This application facilitates user access to telecommunication services, yet discrepancies between user ratings and comments are often found. Therefore, sentiment analysis is needed to more accurately evaluate user satisfaction. This study aims to develop a sentiment analysis system for myXL app reviews using the naïve Bayes algorithm. The data consists of Indonesian-language reviews categorized into two classes, namely positive and negative. Naïve Bayes is chosen for its efficiency and capability to handle large, imbalanced datasets. The study also outlines the research methodology used to build the classification system. Results show that the naïve Bayes model achieves an average accuracy of 87%, precision of 88%, recall of 85%, and f-measure of 86%. In addition to consistent performance across folds, the model is effective in identifying user sentiment. In conclusion, naïve Bayes is a simple yet reliable approach for sentiment analysis in the Indonesian language context and can assist developers in understanding user needs automatically.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment Analysis, Naïve Bayes, myXL, App Reviews, Classification
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Azza Azra Mahendra
Date Deposited:	11 Aug 2025 08:20
Last Modified:	11 Aug 2025 08:20
URI:	http://repository.unsri.ac.id/id/eprint/182676

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