Klasifikasi Sentimen Ulasan Game Roblox di Google Play Store Menggunakan TF-IDF Weighted Word2Vec dan XGBoost
DOI:
https://doi.org/10.35746/jtim.v8i4.1100Keywords:
Sentiment Analysis, Extreme Gradient Boosting, Google Play Store, Roblox, Term Frequency–Inverse Document Frequency Weighted Word2VecAbstract
User reviews on the Google Play Store contain a wide range of opinions regarding the experience of using the Roblox game, both positive and negative. The large number of reviews makes manual sentiment identification inefficient, necessitating a sentiment analysis method capable of processing reviews systematically. This study aims to classify the sentiment of Roblox game reviews using TF-IDF Weighted Word2Vec as the feature representation and Extreme Gradient Boosting (XGBoost) as the classification algorithm. A total of 1,179 reviews were collected via web scraping during November 2025, then manually labeled and validated by an Indonesian language expert. The research stages included preprocessing—such as case folding, cleaning, tokenization, stopword removal, and stemming—followed by feature representation using TF-IDF Weighted Word2Vec with 200-dimensional vectors. The dataset was split in an 80:20 ratio for training and testing, while model optimization was performed using Grid Search with 5-fold Cross Validation. The labeling results consisted of 786 positive reviews and 393 negative reviews. The best configuration yielded an average validation accuracy of 89.5%, while the test data achieved an accuracy of 0.89. The positive class achieved precision, recall, and F1-score of 0.91, 0.92, and 0.91, respectively, while the negative class achieved 0.83, 0.82, and 0.83. These results indicate that the combination of TF-IDF Weighted Word2Vec and XGBoost is capable of effectively classifying the sentiment of Roblox game reviews.
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D. Wiryany, S. Natasha, and R. Kurniawan, “Perkembangan teknologi informasi dan komunikasi terhadap perubahan sistem komunikasi inonesia,” J. Nomosleca, vol. 8, no. 2, pp. 242–252, 2022. https://doi.org/10.26905/nomosleca.v8i2.8821.
D. D. Fitri, “Perkembangan teknologi informasi dan komunikasi dalam pembelajaran pendidikan agama islam,” Arus J. Psikol. dan Pendidik., vol. 2, no. 1, pp. 53–57, 2023. https://doi.org/10.57250/ajpp.v2i1.166.
M. Khairi et al., “Pengaruh teknologi dalam transformasi ekonomi dan bisnis di era digital,” J. Perangkat Lunak, vol. 7, no. 1, pp. 71–78, 2025. https://doi.org/10.32520/jupel.v7i1.3947.
Y. A. Mustofa and I. S. K. Idris, “Ensemble approach to sentiment analysis of google play store app reviews,” Jambura J. Electr. Electron. Eng., vol. 6, no. 2, pp. 181–188, 2024. https://doi.org/10.37905/jjeee.v6i2.25184.
R. Kurniawan, H. O. L. Wijaya, and R. P. Aprisusanti, “Sentiment analysis of google play store user reviews on digital population identity app using k-nearest neighbors,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 13, no. 2, pp. 170–178, 2024. https://doi.org/10.32736/sisfokom.v13i2.2071.
M. R. Hadiyasa and S. M. Isa, “Google play store users comment review classification using svm classifier and random forest,” IJISCS (International J. Inf. Syst. Comput. Sci.), vol. 7, no. 3, p. 200, 2023. https://doi.org/10.56327/ijiscs.v7i3.1584.
N. K. F. P. Dewi, I. G. I. Sudipa, I. W. Sunarya, N. W. J. K. Dewi, and A. S. Kusuma, “Sentiment analysis of roblox game reviews on google play store using lexicon-svm integration,” Sink. J. dan Penelit. Tek. Inform., vol. 9, no. 4, pp. 1863–1867, 2025. https://doi.org/10.63956/jitar.v1i2.31.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 22, pp. 785–794, 2016. https://doi.org/10.1145/2939672.2939785.
A. F¨olling and J. Lepping, “Knowledge discovery for scheduling in computational grids,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 2, no. 4, pp. 287–297, 2012. https://doi.org/10.1002/widm.1060.
M. J. Setiawan and V. R. S. Nastiti, “Dana app sentiment analysis: Comparison of xgboost, svm, and extra trees,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 13, no. 3, pp. 337–345, 2024. https://doi.org/10.32736/sisfokom.v13i3.2239.
F. Rifaldy, Y. Sibaroni, and S. S. Prasetiyowati, “Effectiveness of word2vec and tf-idf in sentiment classification on online investment platforms using support vector machine,” JIPI (Jurnal Ilm. Penelit. Dan Pembelajaran Inform.), vol. 10, no. 2, pp. 863–874, 2025. https://doi.org/10.29100/jipi.v10i2.6055.
Z. A. Khan and V. Rekha, “Fake news detection using tf-idf weighted with word2vec: An ensemble approach,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 3, pp. 1065–1076, 2023. https://ijisae.org/index.php/IJISAE/article/view/3366.
I. G. A. N. Lestari, N. M. R. M. Dewi, K. G. Meiliana, and I. K. A. A. Aryanto, “Effectiveness of adaboost and xgboost algorithms in sentiment analysis of movie reviews,” J. Appl. Informatics Comput., vol. 9, no. 2, pp. 258–264, 2025. https://doi.org/10.30871/jaic.v9i2.9077.
S. M. Mohammad, “Sentiment analysis: Detecting valence, emotions, and other affectual states from text,” in Emot. Meas., pp. 201–237, 2021. https://doi.org/10.1016/B978-0-08-100508-8.00009-6.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” in Global Transitions Proceedings, vol. 3, pp. 91–99, 2022. https://doi.org/10.1016/j.gltp.2022.04.020.
M. A. Palomino, “Evaluating the effectiveness of text pre-processing in sentiment analysis,” Applied Sciences, vol. 12, no. 17, 2022. https://doi.org/10.3390/app12178765.
Y. Albalawi, J. Buckley, and N. S. Nikolov, “Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting arabic health information on social media,” J. Big Data, vol. 8, no. 1, 2021. https://doi.org/10.1186/s40537-021-00488-w.
Gilbert, S. Alam, and M. I. Sulistyo, “Analisis sentimen berdasarkan ulasan pengguna aplikasi mypertamina pada google playstore menggunakan metode na¨ıve bayes,” STORAGE J. Ilm. Tek. dan Ilmu Komput., vol. 2, no. 3, pp. 100–108, 2023. https://doi.org/10.55123/storage.v2i3.2333.
D. Chicco, L. Oneto, and E. Tavazzi, “Eleven quick tips for data cleaning and feature engineering,” PLoS Comput. Biol., vol. 18, no. 12, pp. 1–21, 2022. https://doi.org/10.1371/journal.pcbi.1010718.
V. Zouhar, C. Meister, J. L. Gastaldi, L. Du, M. Sachan, and R. Cotterell, “Tokenization and the noiseless channel,” in Proc. Annu. Meet. Assoc. Comput. Linguist., vol. 1, pp. 5184–5207, 2023. https://doi.org/10.18653/v1/2023.acl-long.284.
S. Sarica and J. Luo, “Stopwords in technical language processing,” PLoS One, vol. 16, no. 8, pp. 1–13, 2021. https://doi.org/10.1371/journal.pone.0254937.
M. U. Albab, Y. K. P., and M. N. Fawaiq, “Optimization of the stemming technique on text preprocessing president 3 periods topic,” J. Transform., vol. 20, no. 2, pp. 1–12, 2023. https://doi.org/10.26623/transformatika.v20i2.5374.
Z. Zhan, “Comparative analysis of tf-idf and word2vec in sentiment analysis: A case of food reviews,” ITM Web Conf., vol. 70, p. 02013, 2025. https://doi.org/10.1051/itmconf/20257002013.
K. W. Church, “Emerging trends: Word2vec,” Nat. Lang. Eng., vol. 23, no. 1, pp. 155–162, 2017. https://doi.org/10.1017/S1351324916000334.
J. A. Ilemobayo et al., “Hyperparameter tuning in machine learning: A comprehensive review,” J. Eng. Res. Reports, vol. 26, no. 6, pp. 388–395, 2024. https://doi.org/10.9734/jerr/2024/v26i61188.
P. Liashchynskyi and P. Liashchynskyi, “Grid search, random search, genetic algorithm: A big comparison for nas,” pp. 1–11, 2019. http://arxiv.org/abs/1912.06059.
S. E. H. Yulianti, O. Soesanto, and Y. Sukmawaty, “Penerapan metode extreme gradient boosting (xgboost) pada klasifikasi nasabah kartu kredit,” J. Math. Theory Appl., vol. 4, no. 1, pp. 21–26, 2022. https://doi.org/10.31605/jomta.v4i1.1792.
S. Sathyanarayanan, “Confusion matrix-based performance evaluation metrics,” African J. Biomed. Res., vol. 27, no. 4, pp. 4023–4031, 2024. https://doi.org/10.53555/ajbr.v27i4s.4345.
G. Zeng, “Invariance properties and evaluation metrics derived from the confusion matrix in multiclass classification,” Mathematics, vol. 13, no. 16, 2025. https://doi.org/10.3390/math13162609.
J. L. Rizky and Z. P. Putra, “Analisis perbandingan algoritma pembelajaran mesin untuk meningkatkan akurasi dan klasifikasi tumor otak,” Indonesian Journal of Applied Informatics, vol. 9, no. 1, pp. 31–44, 2024. https://doi.org/10.20961/ijai.v9i1.90101.
N. Ruhyana, K. Salsabila, A. Agung, and T. Mardiana, “Sentiment analysis of user reviews bri mobile application with gradient boost method,” Jurnal Riset Informatika, vol. 7, no. 230, pp. 48–54, 2023. https://doi.org/10.34288/jri.v7i2.342.
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