Comparative Analysis of Machine Learning Algorithms for Sentiment Classification of Discord App Reviews
Abstract
The increasing use of digital communication applications such as Discord has generated diverse user opinions expressed through reviews on the Google Play Store. This study aims to analyze user sentiment toward the Discord application using text mining and machine learning techniques. A total of 3,000 reviews were collected through web scraping, pre-processed, labeled using a lexicon-based approach with TextBlob, and balanced using the SMOTE-Tomek method. Sentiment classification was performed into positive, negative, and neutral categories using Decision Tree, Logistic Regression, Support Vector Machine (SVM), and an Ensemble method. The Ensemble model achieved the highest accuracy of 98.67%, followed by Decision Tree (96.50%), SVM (95.83%), and Logistic Regression (90.33%). Limitations of this study include the use of lexicon-based sentiment labeling, machine translation from Indonesian to English, and initial class imbalance. Despite this strong performance, the study has limitations related to lexicon-based labeling, translation of reviews into English, and the presence of a highly imbalanced class distribution in the original dataset. Overall, the findings demonstrate that the Ensemble approach effectively improves sentiment classification accuracy and can support data-driven decision-making in application development.
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References
R. Q. Rohmansa, N. Pratiwi, and M. J. Palepa, “Analisis Sentimen Ulasan Pengguna Aplikasi Discord Menggunakan Metode K-Nearest Neighbor,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 1, pp. 368–378, Feb. 2024, doi: 10.29100/jipi.v9i1.4943.
P. W. Ciady and S. Hariyanto, “Implementasi Sentimen Emosi Pada Lirik Lagu Menggunakan Bot Discord Dengan Metode Analisis Sentimen Berbasis Leksikon,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 5, Okt. 2024, doi: 10.36040/jati.v8i5.11103
M. Minarni, “Pelatihan Pemanfaatan Aplikasi Discord Sebagai Kelas Virtual Bagi Guru Se-Kotawaringin Timur,” Dinamisia : Jurnal Pengabdian Kepada Masyarakat, vol. 6, no. 4, Aug. 2022, doi: 10.31849/dinamisia.v6i4.5865.
S. N. Salsabila, B. N. Sari, and R. Mayasari, “Klasifikasi Ulasan Pengguna Aplikasi Discord Menggunakan Metode Information Gain Dan Naive Bayes Classifier,” Infotech journal, vol. 9, no. 2, pp. 383–392, Jul. 2023, doi: 10.31949/infotech.v9i2.6277.
M. Rizky Pratama, Y. R. Ramadhan, and M. A. Komara, “Analisis Sentimen BRImo dan BCA Mobile Menggunakan Support Vector Machine dan Lexicon Based”, Jutisi: Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi, vol. 12, no. 3, pp. 1439-1450, Nov. 2023, doi: 10.35889/jutisi.v12i3.143.
S. E. Situmeang and N. P. Savina, “Analisis Perbandingan Metode Decision Tree, Random Forest, dan Support Vector Machine (SVM) dalam Memprediksi Kesehatan Janin,” Dept. Stat., Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Tech. Rep., pp. 1–8, 2024.
RA, R. D. Y., R. A. Muhadi, A. Fitrianto, and P. Silvianti, “Analisis Regresi Logistik Biner dan Random Forest untuk Prediksi Faktor-Faktor Stunting di Pulau Jawa,” Euler: Jurnal Ilmiah Matematika, Sains dan Teknologi, vol. 13, no. 2, pp. 147–156, 2025.
A. Diki Prasetyo, F. T. Anggraeny, and R. Mumpuni, “Metode Ensemble Weighted Voting Untuk Deteksi Risiko Diabetes”, JIP (Jurnal Informatika Polinema), vol.11, no. 4, pp. 385-390, Agustus. 2025, doi: 10.33795/jip.v11i4.7353
V. I. Yani, A. Aradea, and H. Mubarok, “Optimasi Prakiraan Cuaca Menggunakan Metode Ensemble pada Naïve Bayes dan C4.5,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, Dec. 2022, doi: 10.28932/jutisi.v8i3.5455.
P. T. Prasetyaningrum, P. Purwanto, and A. F. Rochim, “Consumer Behavior Analysis in Gamified Mobile Banking: Clustering and Classifier Evaluation,” Online) Journal of System and Management Sciences, vol. 15, no. 2, pp. 290–308, 2025, doi: 10.33168/JSMS.2025.0218.
L. Maretva Cendani and A. Wibowo, “Perbandingan Metode Ensemble Learning pada Klasifikasi Penyakit Diabetes,” vol. 13, no. 1, Mei. 2022, doi: 10.14710/jmasif.13.1.42912
Steven Joses, D. Yulvida, and S. Rochimah, “Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier,” Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 72–80, Jun. 2024, doi: 10.52158/jacost.v5i1.741.
S. D. Parameswari et al., “Studi Perbandingan Naïve Bayes dan Support Vector Machine (SVM) dalam Analisis Sentimen Pengguna Metaverse,” Jurnal Teknologi dan Manajemen Industri Terapan (JTMIT), vol. 4, no. 3, pp 1059-1065, Sept. 2025, doi: 10.55826/jtmit.v4i3.1122
Sudarto and Kusrini, “JIP (Jurnal Informatika Polinema) Klasifikasi Tsunami Gempa Bumi Dengan Teknik Stacking Ensemble Machine Learning”, vol. 10, no. 4, pp. 511-520, August. 2024, doi: 10.33795/jip.v10i4.5655
O. I. Gifari, M. Adha, I. Rifky Hendrawan, F. Freddy, and S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” JIFOTECH (Journal of Information Technology, vol. 2, no. 1, pp 36-40, Maret. 2022, doi: 10.46229/jifotech.v2i1.330
G. R. Ati and P. T. Prasetyaningrum, “Analysis of Community Sentiment Towards Free Nutrition Meal Programs on Twitter Using Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, and Ensemble Methods,” Journal of Information Systems and Informatics, vol. 7, no. 2, pp. 1443–1460, Jul. 2025, doi: 10.51519/journalisi.v7i2.1098.
A. R. Isnain, H. Sulistiani, B. M. Hurohman, A. Nurkholis, and S. Styawati, “Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen,” JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 8, no. 2, pp. 299–303, 2022.
R. Perangin-angin, E. Julia Gunawati Harianja, I. Kelana Jaya, and B. Rumahorbo, “Penerapan Algoritma Safe-Level-SMOTE Untuk Peningkatan Nilai G-Mean Dalam Klasifikasi Data Tidak Seimbang,” METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, vol. 4, no. 1, 2020, doi: 10.46880/jmika.vol4no1.pp67 72.
H. K. Saka and P. T. Prasetyaningrum, “Sentiment Analysis and Classification of User Reviews of the ‘Access by KAI’ Application Using Machine Learning Methods to Improve Service Quality,” Journal of Information Systems and Informatics, vol. 7, no. 2, pp. 1418–1442, Jun. 2025, doi: 10.51519/journalisi.v7i2.1099.
I. D. Fareza and E. T. E. Handayani, “Analisis Sentimen Kualitas Aplikasi Discord Menggunakan Algoritma Naïve Bayes dan Support Vector Machine,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 3, pp. 4564–4571, 2025.
S. Usman and F. Aziz, “Analisis Perilaku Pelanggan menggunakan Metode Ensemble Logistic Regression,” Jurnal Teknologi dan Ilmu Komputer Prima (Jutikomp), vol. 6, no. 2, pp. 90–97, 2023, doi: 10.34012/jutikomp.v6i2.4258
C. Cahyaningtyas, Y. Nataliani, and I. R. Widiasari, “Analisis sentimen pada rating aplikasi Shopee menggunakan metode Decision Tree berbasis SMOTE,” AITI: Jurnal Teknologi Informasi, vol. 18, no. Agustus, pp. 173–184, 2021, doi: 10.24246/aiti.v18i2.173-184.
E. R. Putri, D. A. Prasetya, and A. Junaidi, “Klasifikasi Perulangan Kanker Tiroid Menggunakan Stack Ensemble dan SMOTE,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 3, pp. 4211–4216, 2025, doi: 10.36040/jati.v9i3.13616.
F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 9, pp. 4305–4313, 2022.
M. Idris, A. Rifai, and K. D. Tania, “Sentiment Analysis of Tokopedia App Reviews using Machine Learning and Word Embeddings,” sinkron, vol. 9, no. 1, pp. 210–219, Jan. 2025, doi: 10.33395/sinkron.v9i1.14278.
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