Analisis  Analisis Sentimen Ulasan eFootball pada Google Play Store Menggunakan Multinomial Naive Bayes dan Support Vector Machine

Mohammad Umar Sasongko Sasongko; Bambang  Irawan

doi:10.31850/jsilog.v6i1.4282

Mohammad Umar Sasongko Sasongko Teknik Informatika, Universitas Muhadi Setiabudi Brebes
Bambang Irawan Teknik Informatika, Universitas Muhadi Setiabudi Brebes

DOI: https://doi.org/10.31850/jsilog.v6i1.4282

Keywords: Sentiment Analysis, eFootball, Multinomial Naive Bayes, SVM

Abstract

In the digital era, the use of mobile applications is increasing, so it is important to understand user satisfaction and dissatisfaction with the applications used. One of the popularmobile games in Indonesia is eFootball 2026, which has a lot of reviews from players on the Google Play Store. The large number of reviews allows sentiment analysis to be carried out to find out user opinions on the quality of the application. This study aims to analyze the sentiment of eFootball application user reviews using the Multinomial Naive Bayes method and Support Vector Machine. Review data is processed through the stages of text preprocessing, feature extraction using TF-IDF, and class imbalance handling with SMOTE. Model evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the Multinomial Naive Bayes method produced an accuracy of 76.72%, while the Support Vector Machine obtained an accuracy 74.92%, with relatively balanced precision, recall, and F1-score values. Based on these results, it can be concluded that the Multinomial Naive Bayes method has a better performance in analyzing the sentiment of eFootball app reviews on the Google Play Store and can be used as a basis for evaluation for future app development.

References

Amirkhalili, Y., & Wong, H. Y. (2024). Banking on Feedback : Text Analysis of Mobile Banking iOS and Google App Reviews.

Anggara, M. B., Informasi, F. T., & Bandung, U. B. (2025). PERBANDINGAN NAÏVE BAYES DAN SVM DALAM ANALISIS SENTIMEN. 20, 32–42.

Dwi, P., Maulidiana, R., Vitianingsih, A. V., Kacung, S., & Maukar, A. L. (2024). Comparative Analysis of SVM and NB Algorithms in Evaluating Public Sentiment on Supreme Court Rulings. 13, 230–236.

Jurnal, S., Informasi, S., Tika, Y., Sholiha, R., Asjad, L., Nabilah, M., Teknik, F., Informasi, S., Bina, U., Informatika, S., & Tangerang, K. (2025). Analisis Sentimen Aplikasi Liputan6 . Com pada Ulasan Pengguna di Google Playstore dengan Menggunakan Algoritma Support Vector Machine ( Svm ) dan Naïve Bayes.

Lundam, I. S., & Darmawan, J. B. B. (2024). Analisis Sentimen Ulasan Aplikasi Gojek Di Google Play Store Menggunakan Multinomial Naïve Bayes. 2024(November), 149–156.

Nawulansih, D. F., Santi, N. C., Aristia, I., Informatika, T., Nahdlatul, U., Sunan, U., Bojonegoro, G., Informasi, S., Nahdlatul, U., Sunan, U., & Bojonegoro, G. (2025). Analisis Sentimen Ulasan Aplikasi DANA di Google Play Store : Penerapan Support Vector Machine dan Synthetic Minority Over-sampling Technique Sentiment Analysis of DANA Application Reviews on Google Play Store : Implementation of Support Vector Machine an. 5(9), 2660–2671.

Nouval, M., Habibi, F. M., Rahmi, A., Dawam, M., Bittaqwa, A., Agustianto, R., & Hasan, F. N. (2026). Analisis Sentimen Ulasan Aplikasi Maxim di Google Play Store Menggunakan Algoritma Support Vector Machine.

Nurian, A., Sari, B. N., Karawang, U. S., & Timur, T. (2023). Analisis sentimen ulasan pengguna aplikasi google play menggunakan naïve bayes. 11(3), 829–835.

R, S. M. M., & Effendy, Y. S. (2025). Analisis Sentimen Ulasan Game Simulator Indonesia di Google Play Store Menggunakan Algoritma Naive Bayes. 7(2), 169–178. https://doi.org/10.35580/variansiunm336

Ranjan, S., & Bhaba, H. (2020). Comparative Sentiment Analysis of App Reviews. 1–10.

Rhomaningtias, L., & al., et. (2025). Analisis Sentimen Ulasan Aplikasi SMILE Indonesia Menggunakan Naïve Bayes dan SVM. HOAQ: Jurnal Teknologi Informasi, 4(November), 63–72. https://publikasi.uyelindo.ac.id

Sarimole, F. M. (2024). Analisis Sentimen Terhadap Aplikasi Satu Sehat Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine. 5(3), 783–790.

Setiawan, N. R., & Kaburuan, E. R. (2023). Sentimen Analisis Review Aplikasi Digital Korlantas Pada Google Play Store Menggunakan Metode SVM. 12, 105–116.

Syaifudin, A., Rafsanjani, A., Fithri, D. L., Studi, P., Informasi, S., Teknik, F., & Kudus, U. M. (2025). Analisis Sentimen Ulasan Pengguna Aplikasi KitaLulus pada Google Play Store dengan menggunakan algoritma Support Vector Machine ( SVM ) Sentiment Analysis of User Reviews of the KitaLulus Application on Google Play Store using the Support Vector Machine (. 14(Cv), 2519–2530.

Zuhdi, H. N., & Prasetiyo, B. (2025). Sentiment Analysis On Ipusnas Application Reviews In Google Play Store Using Naive Bayes Classifier Analisis Sentimen pada Ulasan Aplikasi iPusnas di Google Play Store Menggunakan Naive Bayes Classifier. 5(1), 12–19.