Sentiment Analysis of Mamikos Application User Reviews using the Naive Bayes Classifier Algorithm

Amsal Tampubolon; Erich Ricardo; Sardo Pardingotan Sipayung

doi:10.59934/jaiea.v5i2.2084

Authors

Amsal Tampubolon Universitas Katolik Santo Thomas
Erich Ricardo Universitas Katholik Santo Thomas
Sardo Pardingotan Sipayung Universitas Katholik Santo Thomas

DOI:

https://doi.org/10.59934/jaiea.v5i2.2084

Keywords:

Sentiment, Algorithm, Naive Bayes, Google Play Store

Abstract

Mamikos, a boarding house search application, has grown to be among Indonesia's most well-liked sites. User reviews on the Google Play Store provide valuable data regarding user satisfaction. However, the large volume of reviews makes it difficult for developers to analyze sentiments manually. This study seeks to do sentiment analysis on evaluations of the Mamikos application to categorize user thoughts as positive or negative sentiments. The method used is the Naive Bayes Classifier with TF-IDF (Term Frequency-Inverse Document Frequency) feature weighting. The research stages include data collection (scraping), text preprocessing (including cleaning and stopword removal using the Sastrawi library), and classification. The test results show that the Naive Bayes algorithm can classify sentiments with an accuracy of 87.96 %. This research is expected to provide insights for application developers regarding aspects that need improvement based on user complaints.

Downloads

Download data is not yet available.

References

Fitri Nur Utami, Hani Dewi Ariessanti, Riya Widayanti, and Arief ichwani, “Klasifikasi Sentimen untuk Prediksi Churn Pengguna Aplikasi Mamikos Menggunakan Support Vector Machine (SVM) dan Naïve Bayes,” J. Ilmu Multidisiplin, vol. 4, no. 4, pp. 2213–2223, 2025.

K. Vasa, “Sentiment Analysis of Google Play Store Reviews Using Naive Bayes and Decision Tree,” Int. J. Comput. Appl., vol. 182, no. 48, pp. 15–19, 2019.

B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.

V. Narayanan, I. Arora, and A. Bhatia, “Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model,” in Intelligent Data Engineering and Automated Learning -- IDEAL 2013, 2013, pp. 194–201.

C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008.

J. Han, M. Kamber, and J. Pe, Data Mining: Concepts and Techniques. 2011. doi: 10.1016/C2009-0-61819-5.

M. Allahyari et al., “A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques,” 2017, [Online]. Available: http://arxiv.org/abs/1707.02919

F. Z. Tala, “A Study of Stemming Effects on Information Retrieval in Indonesian Language,” Institute for Logic, Language and Computation, Universiteit van Amsterdam, Amsterdam, Netherlands, 2003.

. Muammar Khadapi, & Pakpahan, V. M. (2024). Analisis Sentimen Berbasis Jaringan LSTM dan BERT terhadap Diskusi Twitter tentang Pemilu 2024. JUKI : Jurnal Komputer Dan Informatika, 6(2), 130–137. Retrieved from https://ioinformatic.org/index.php/JUKI/article/view/681