Penerapan Algoritma Random Forest untuk Menganalisis Ulasan Aplikasi Spotify pada Google Play

Authors

  • Gina Purnama Insany Program Studi Teknik Informatika, Universitas Nusa Putra, Indonesia
  • Ivana Lucia Kharisma Program Studi Teknik Informatika, Universitas Nusa Putra, Indonesia
  • Rosmawati Rosmawati Program Studi Teknik Informatika, Universitas Nusa Putra, Indonesia

DOI:

https://doi.org/10.29408/edumatic.v8i2.26394

Keywords:

random forest, reviews, sentiment analysis, spotify, web scraping

Abstract

The development of internet technology and mobile devices has played an important role in transforming the music industry and driving the emergence of music streaming services, such as Spotify. This research aims to understand Spotify users' preferences and expectations through sentiment analysis of user reviews. The review data was retrieved from Google Play Store using web scraping technique, including rivew and rating of 1000 Indonesian and 1000 English reviews. Random Forest algorithm was used to model the sentiment of the reviews, with the analysis process including data collection, labeling, preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, oversampling, modeling, and evaluation with confusion matrix. Algorithm testing was conducted using 70% training data and 30% test data. The classification evaluation results of the Random Forest algorithm showed a model accuracy of 88.4% for Indonesian reviews and 93.6% for English reviews. These findings show that the Random Forest algorithm is effective for sentiment analysis in a multilingual context and can help app developers improve service quality based on user sentiment. This research was also deployed using Streamlit, enabling access and usage by users for fast and interactive sentiment analysis.

References

Aldean, M. Y., Paradise, P., & Setya Nugraha, N. A. (2022). Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 di Twitter Menggunakan Metode Random Forest Classifier (Studi Kasus: Vaksin Sinovac). Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 4(2), 64–72. https://doi.org/10.20895/inista.v4i2.575

APJII. (2024). Survei Internet APJII (Asosiasi Penyedia Jasa Internet Indonesia) 2024. Survei. https://survei.apjii.or.id/survei/group/9

Ginabila, & Fauzi, A. (2023). Analisis Sentimen Terhadap Pemutar Musik Online Spotify Dengan Algoritma Naive Bayes dan Support Vector Machine. Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika, 6(2), 111–122. https://doi.org/10.47324/ilkominfo.v6i2.180

Habibi, C. B., & Irwansyah, I. (2020). Konsumsi Dan Produksi Musik Digital Pada Era Industri Kreatif. Metacommunication: Journal of Communication Studies, 5(1), 23-37. http://dx.doi.org/10.20527/mc.v5i1.7449

Hartatik, Koibur, M. E., Insany, G. P., & et all. (2023). SAINS DATA: Strategi, Teknik, dan Model Analisis Data (Cetakan Pertama). Bandung: Kaizen Media Publishing.

Irawan, D., Perkasa, E. B., Yurindra, Y., Wahyuningsih, D., & Helmud, E. (2021). Perbandingan Klassifikasi SMS Berbasis Support Vector Machine, Naive Bayes Classifier, Random Forest dan Bagging Classifier. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 10(3), 432–437. https://doi.org/10.32736/sisfokom.v10i3.1302

Karfindo, Turaina, R., & Saputra, R. (2023). Optimalisasi Klasifikasi Umpan Balik Mahasiswa Terhadap Layanan Kampus dengan Sinergi Random Forest dan Smote. Jurnal Nasional Komputasi Dan Teknologi Informasi, 6(6), 820-827. https://doi.org/10.32672/jnkti.v6i6.7269

Kurnia, S., Prabowo, H. G., & Suharmanto, B. (2022). Klasifikasi Tweet Cyberbullying dengan Menggunakan Algoritma Random Forest. Seminar Nasional Mahasiswa Ilmu Komputer Dan Aplikasinya (SENAMIKA), 3(2), 753-764.

Lesmana, W. P., & Wijaya, A. (2023). Analisis Sentimen Menggunakan Metode Naive Bayes dan Support Vector Machine pada Ulasan Aplikasi Joox Music. Jurnal Ilmiah Sain Dan Teknologi, 1(2), 110–120.

Mahendra, S., & Syofian, S. (2023). Penggunaan Algoritma Support Vector Machine (SVM) untuk Menganalisis Sentimen dari Ulasan Pelanggan Terhadap Layanan Kurir J&T Express di Google Play Store. JURNAL SAINS & TEKNOLOGI, 13(2), 29–36. https://doi.org/10.70746/jstunsada.v13i2.450

Maulana, R., Voutama, A., & Ridwan, T. (2023). Analisis Sentimen Ulasan Aplikasi MyPertamina pada Google Play Store menggunakan Algoritma NBC. Jurnal Teknologi Terpadu, 9(1), 42–48. https://doi.org/10.54914/jtt.v9i1.609

Mustaqim, K., Maresti, F. A., & Dewi, I. N. (2024). Analisis Sentimen Ulasan Aplikasi PosPay untuk Meningkatkan Kepuasan Pengguna dengan Metode K-Nearest Neighbor (KNN). Edumatic: Jurnal Pendidikan Informatika, 8(1), 11–20. https://doi.org/10.29408/edumatic.v8i1.24779

Ndapamuri, A. M., Manongga, D., & Iriani, A. (2023). Analisis Sentimen Ulasan Aplikasi Tripadvisor Dengan Metode Support Vector Machine, K-Nearest Neighbor, Dan Naive Bayes. Jurnal Inovtek Polbeng Seri Informatika, 8(1), 127-140. https://doi.org/10.35314/isi.v8i1.3260

Noviani, D., Pratiwi, R., Silvianadewi, S., Benny Alexandri, M., & Aulia Hakim, M. (2020). Pengaruh Streaming Musik Terhadap Industri Musik di Indonesia. JURNAL BISNIS STRATEGI, 29(1), 14–25. https://doi.org/10.14710/jbs.29.1.14-25

Putra, A. D. A., & Juanita, S. (2021). Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 8(2), 636–646. https://doi.org/10.35957/jatisi.v8i2.962

Rahayu, A. S., Fauzi, A., & Rahmat, R. (2022). Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify. Jurnal Sistem Komputer Dan Informatika (JSON), 4(2), 349. http://dx.doi.org/10.30865/json.v4i2.5398

Rahayu, S., MZ, Y., Bororing, J. E., & Hadiyat, R. (2022). Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP. Edumatic: Jurnal Pendidikan Informatika, 6(1), 98–106. https://doi.org/10.29408/edumatic.v6i1.5433

Sihombing, L. O., Hannie, H., & Dermawan, B. A. (2021). Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier. Edumatic: Jurnal Pendidikan Informatika, 5(2), 233–242. https://doi.org/10.29408/edumatic.v5i2.4089

Sondakh, Debby E, Taju, S. W., Tene, M. G., & Pangaila, A. E. T. (2023). Sistem Analisis Sentimen Ulasan Aplikasi Belanja Online Menggunakan Metode Ensemble Learning. CogITo Smart Journal, 9(2), 280–291. https://doi.org/10.31154/cogito.v9i2.525.280-291

Yunita, I. R., Baihaqi, W. M., Damayanti, A. S. T., & Akhaerunnisa, L. (2023). Analysis of Classification Algorithm Performance on User Review Sentiment of the Muamalat DIN Application. CogITo Smart Journal, 9(2), 241–251. https://doi.org/10.31154/cogito.v9i2.511.241-251

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Published

2024-12-19

Issue

Vol. 8 No. 2 (2024): Edumatic: Jurnal Pendidikan Informatika

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Articles

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Copyright (c) 2024 Gina Purnama Insany, Ivana Lucia Kharisma, Rosmawati Rosmawati

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