Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews
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Abstract
The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.
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Articles
References
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[4] R. Wahyudi and G. Kusumawardana, “Analisis Sentimen pada Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine,” Jurnal Informatika, vol. 8, no. 2, pp. 200–207, Sep. 2021, doi: 10.31294/ji.v8i2.9681.
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[7] M. Raffi, A. Suharso, and I. Maulana, “Analisis Sentimen Ulasan Aplikasi Binar Pada Google Play Store Menggunakan Algoritma Naïve Bayes,” INTECOMS: Journal of Information Technology and Computer Science, vol. 6, no. 1, pp. 450–462, Jun. 2023, doi: 10.31539/intecoms.v6i1.6117.
[8] R. Ramadhan, M. Afdal, I. Permana, and M. Jazman, “Analisis Sentimen pada Ulasan Aplikasi Maxim di Google Play Store dengan K-Nearest Neighbor,” JURIKOM (Jurnal Riset Komputer), vol. 10, no. 3, 2023.
[9] D. Y. Lakoro, E. Utami, and D. Ariatmanto, “Sentiment Analysis of BNI Mobile Application Using The K-Nearest Neighbor Algorithm (KNN) With Particle Swarm Optimization (PSO) Feature Selection,” INTECOMS: Journal of Information Technology and Computer Science, vol. 6, no. 2, pp. 948–953, Nov. 2023, doi: 10.31539/intecoms.v6i2.7912.
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[11] M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 139–150, Nov. 2021, doi: 10.30812/matrik.v21i1.1092.
[12] A. F. N. Azizah and V. P. Ramadhan, “Comparison of Naïve Bayes and K-N-N in Sentiment Analysis on Twitter Regarding the Victory of Candidate Pair 02,” J-Intech: Journal of Information and Technology, vol. 12, no. 2, pp. 228–237, Dec. 2024.
[13] M. Furqan, Sriani, and S. M. Sari, “Analisis Sentimen Menggunakan K-Nearest Neighbor Terhadap New Normal Masa Covid-19 di Indonesia,” Techno.COM, vol. 21, pp. 52–61, Feb. 2022.
[14] S. Alfaris and Kusnawi, “Komparasi Metode KNN dan Naive Bayes Terhadap Analisis Sentimen Pengguna Aplikasi Shopee,” Indonesian Journal of Computer Science, vol. 12, no. 5, Oct. 2023, doi: 10.33022/ijcs.v12i5.3304.
[15] G. Darmawan, S. Alam, and M. I. Sulistyo, “Analisis Sentimen Berdasarkan Ulasan Pengguna Aplikasi Mypertamina Pada Google Playstore Menggunakan Metode Naïve Bayes,” STORAGE – Jurnal Ilmiah Teknik dan Ilmu Komputer, vol. 2, no. 3, pp. 100–108, 2023.
[16] M. Riski, M. Fikry, and Yusra, “Klasifikasi Sentimen Ulasan Aplikasi WhatsApp di Play Store Menggunakan Metode K-Nearest Neighbor,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 1, pp. 438–444, 2023, doi: 10.30865/klik.v4i1.1050.
[17] P. Astuti and N. Nuris, “Penerapan Algoritma KNN Pada Analisis Sentimen Review Aplikasi Peduli Lindungi,” Computer Science (CO-SCIENCE), vol. 2, no. 2, pp. 137–142, Jul. 2022, doi: 10.31294/coscience.v2i2.1258.