Comparative Sentiment Analysis of Profits Anywhere Application Reviews Using KNN and Naïve Bayes

Authors

  • Yurindra Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Chandra Kirana Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Dian Novianto Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Tri Sugihartono Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Muhammad Ilham Institut Sains dan Bisnis Atma Luhur, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i4.1658

Keywords:

Sentiment Analysis, K-Nearest Neighbor, Naïve Bayes, Fintech Application, Google Play Store Reviews

Abstract

User reviews on Google Play Store provide valuable information regarding application service quality and user satisfaction. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms for sentiment analysis of reviews of the Profits Anywhere application, a digital business platform offering affiliate marketing and financial services. A total of 1,037 reviews were collected through web scraping and subsequently underwent data cleaning and validation, resulting in 957 valid reviews used in the analysis, consisting of 674 positive and 283 negative reviews. The dataset was subjected to a comprehensive text preprocessing pipeline, including text cleaning, case folding, tokenization, stopword removal, and stemming, followed by feature representation using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. The data were partitioned using an 80:20 stratified train–test split, while hyperparameter optimization was conducted using GridSearchCV with 5-fold stratified cross-validation. Experimental results demonstrate that KNN outperformed Naïve Bayes on the evaluated dataset and experimental configuration, achieving an accuracy of 92.19%, weighted precision of 92.23%, weighted recall of 92.19%, and weighted F1-score of 92.21%. In contrast, Naïve Bayes achieved an accuracy of 88.54%, weighted precision of 89.04%, weighted recall of 88.54%, and weighted F1-score of 88.70%. These findings provide empirical evidence regarding algorithm selection for sentiment analysis of application reviews in the fintech and digital business domains.

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References

[1] M. Jorayeva, A. Akbulut, C. Catal, And A. Mishra, “Machine Learning-Based Software Defect Prediction For Mobile Applications: A Systematic Literature Review,” Sensors, Vol. 22, No. 7, Pp. 2–17, 2022, Doi: 10.3390/S22072551.

[2] A. A. Qureshi, M. Ahmad, S. Ullah, M. N. Yasir, F. Rustam, And I. Ashraf, “Performance Evaluation Of Machine Learning Models On Large Dataset Of Android Applications Reviews,” Multimed. Tools Appl., Vol. 82, No. 24, Pp. 37197–37219, 2023, Doi: 10.1007/S11042-023-14713-6.

[3] O. Aljeezani, D. Alomari, And I. Ahmad, “Arabic App Reviews: Analysis And Classification,” Acm Trans. Asian Low-Resour. Lang. Inf. Process., Vol. 24, No. 2, Feb. 2025, Doi: 10.1145/3708987.

[4] S. A. Memon, M. A. Mahar, A. Kehar, S. Oad, And I. Ahmed, “User Experience Enhancement Through Sentiment Analysis: A Machine Learning Approach To App Reviews,” Int. J. Inf. Syst. Comput. Technol., Vol. 4, No. 2, Pp. 37–46, 2025, Doi: 10.58325/Ijisct.004.02.0131 User.

[5] A. Arora, V. Soni, M. Sharma, P. Kulhari, And P. Vats, “The Role Of Digital Marketing Analytics In Enhancing Passive Income From E-Commerce Platforms,” In In: Atulya K., N., Dharm Singh, J., Durgesh Kumar, M., Joshi, A. (Eds) Intelligent Sustainable Systems. Worlds4 2025. Lecture Notes In Networks And Systems, 2026, Pp. 1–11. Doi: 10.1007/978-3-032-11521-8_1.

[6] L. R. Ali, B. N. Shaker, And S. A. Jebur, “An Extensive Study Of Sentiment Analysis Techniques: A Survey,” In Aip Conference Proceedings, 2023, Vol. 2591, P. 2591. Doi: 10.1063/5.0119604.

[7] Y. Yuniarthe, A. Syarif, I. M. Shofi, And Fatimah Fahurian, “Sentiment Analysis Of Twitter Discussions About Lampung Robusta Coffee: A Comparative Study Of Machine Learning Algorithms With Svm As The Optimal Model,” J. Tek. Inform., Vol. 18, No. 2, Pp. 339–349, 2025, [Online]. Available: Https://Journal.Uinjkt.Ac.Id/Index.Php/Ti/Article/View/41316

[8] P. Rindiani Et Al., “Penerapan Machine Learning Untuk Analisis Sentimen Agoda Dengan Algoritma Knn , Naive Bayes Dan Svm,” Jumistik J. Manaj. Inform. Sist. Inf. Dan Teknol. Inform., Vol. 4, No. 2, Pp. 664–672, 2025, Doi: 10.70247/Jumistik.V4i2.232.

[9] A. A. Purnama And Y. R. Sipayung, “Sentiment Analysis Of Public Service Using Naïve Bayes Classifier,” J. Inf. Syst. Informatics, Vol. 7, No. 3, Pp. 2439–2457, 2025, Doi: 10.51519/Journalisi.V7i3.1207.

[10] A. Nurfaida, R. Y. A. Syabana, T. Abimanyu, And N. Husufa, “Web-Based Inventory Application And Prediction Using Naive Bayes Algorithm (Case Study: Puskesmas Kecamatan Sawah Besar),” J. Inf. Syst. Informatics, Vol. 4, No. 4, Pp. 864–878, 2022, Doi: 10.51519/Journalisi.V4i4.348.

[11] A. K. Laturiuw And Y. A. Singgalen, “Sentiment Analysis Of Raja Ampat Tourism Destination Using Crisp-Dm: Svm, Nbc, Dt, And K-Nn Algorithm,” J. Inf. Syst. Informatics, Vol. 5, No. 2, Pp. 518–535, 2023, Doi: 10.51519/Journalisi.V5i2.490.

[12] M. Febima And L. Magdalena, “Predictive Analytics On Shopee For Optimizing Product Demand Prediction Through K-Means Clustering And Knn Algorithm Fusion,” J. Inf. Syst. Informatics, Vol. 6, No. 2, Pp. 751–765, 2024, Doi: 10.51519/Journalisi.V6i2.720.

[13] A. K. Iman And E. I. H. Ujianto, “Analisis Sentimen Pemindahan Ibu Kota Indonesia Menggunakan K-Nearest Neighbor Sentiment Analysis Of The Relocation Of Indonesia ’ S Capital Using K-Nearest Neighbor,” J. Pendidik. Dan Teknol. Indones., Vol. 4, No. 12, Pp. 759–768, 2024, Doi: 10.52436/1.Jpti.546.

[14] T. M. Fadli And A. S. Puspaningrum, “Analisis Sentimen Terhadap Kenaikan Gaji Guru Honorer Menggunakan Algoritma Naïve Bayes Sentiment Analysis Towards The Increase In Honorary Teachers ’ Salary Using The Naïve Bayes Algorithm,” J. Pendidik. Dan Teknol. Indones., Vol. 5, No. 3, Pp. 635–644, 2025, Doi: 10.52436/1.Jpti.689.

[15] A. Bhalla, “Comparative Analysis Of Text Classification Using Svm, Naive Bayes, And Knn Models,” In 2023 5th International Conference On Inventive Research In Computing Applications (Icirca), 2023, Pp. 878–883. Doi: 10.1109/Icirca57980.2023.10220728.

[16] P. C. E. And S. Mata-Domingo, “Sentiment Analysis On Product Reviews: A Machine Learning Approach For E- Commerce Platforms,” Int. J. Manag. Entrep. Res., Vol. 8, No. 1, Pp. 148–161, 2026, Doi: 10.51594/Ijmer.V8i1.2184.

[17] N. L. W. S. R. Ginantra, C. P. Yanti, G. D. Prasetya, I. B. G. Sarasvananda, And I. K. A. G. Wiguna, “Analisis Sentimen Ulasan Villa Di Ubud Menggunakan Metode Naive Bayes, Decision Tree, Dan K-Nn,” Janapati (Jurnal Nas. Pendidik. Tek. Inform., Vol. 11, No. 3, Pp. 205–216, 2024, Doi: 10.23887/Janapati.V11i3.49450.

[18] S. Thiang, W. Chandra, Ferawaty, And M. A. S. Manulang, “Comparison Of Naïve Bayes Classifier And K-Nearest Neighbor Algorithms In Sentiment Analysis On Social Media X With Vader Lexicon,” Jiko (Jurnal Inform. Dan Komputer), Vol. 8, No. 2, Pp. 85–93, 2025, Doi: 10.33387/Jiko.V8i2.9865.

[19] V. And P. S. Atina, “Sentiment Analysis Of Grab App Reviews With Machine Learning Approach,” In Proceeding Of International Conference On Science, Health, And Technology, 2024, Pp. 395–402. Doi: 10.47701/Icohetech.V5i1.4218.

[20] S. Putri, A. Ati, P. Muharani, And R. Qori, “Sentiment Analysis Of Gojek User Reviews Using Tf-Idf And Machine Learning In Orange Platform,” Jsce (Journal Syst. Comput. Eng., Vol. 6, No. 4, Pp. 296–305, 2025, Doi: 10.61628/Jsce.V6i4.2062.

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Published

2026-08-22

Issue

Vol. 8 No. 4 (2026): August

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