Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine

  • Muhammad Diki Hendriyanto Universitas Singaperbangsa Karawang
  • Azhari Ali Ridha Universitas Singaperbangsa Karawang
  • Ultach Enri Universitas Singaperbangsa Karawang

Abstract

MOLA is one of the video streaming platform applications on the google play store. The mola application has been downloaded 5 million times but only has a 3.2 rating. On the Google Play Store app rating is followed by user reviews of the app. There are quite a lot of reviews that are unstructured and contain opinions from users about their satisfaction with the application so that it is often taken into consideration by potential users to choose the application used. Based on this, sentiment analysis was carried out using the Support Vector Machine algorithm to find out how the sentiments of users towards the MOLA application on the google play store were carried out. This study uses the Knowledge Discovery in Database (KDD) method. The data used is a review of the MOLA application with as many 520 data consisting of 312 positive reviews and 208 negative reviews. The best results are obtained in scenario 1 (90:10) using the RBF (Radial Basis Function) kernel which produces 92.31% accuracy, 96.3% precision, 89.66% recall, and 92.86% f1-score.

 

Keywords: Sentiment Analysis, Support Vector Machine, MOLA

References

Fransiska, S., & Irham Gufroni, A. (2020). Sentiment Analysis Provider by.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method. Scientific Journal of Informatics, 7(2), 2407–7658. http://journal.unnes.ac.id/nju/index.php/sji
Husada, H. C., & Paramita, A. S. (2021). Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM). Teknika, 10(1), 18–26. https://doi.org/10.34148/teknika.v10i1.311
Ilmawan, L. B., & Mude, M. A. (2020). Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store. ILKOM Jurnal Ilmiah, 12(2), 154–161. https://doi.org/10.33096/ilkom.v12i2.597.154-161
Irfani, F. F., Triyanto, M., Hartanto, A. D., & Kusnawi. (2020). Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine. JBMI (Jurnal Bisnis, Manajemen, Dan Informatika), 16(3), 258–266. https://doi.org/10.26487/jbmi.v16i3.8607
Iskandar, J. W., & Nataliani, Y. (2021). Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek. JURNAL RESTI, 5(158), 1120–1126.
Mukarramah, R., Atmajaya, D., & Ilmawan, L. B. (2021). Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter. ILKOM Jurnal Ilmiah, 13(2), 168–174. https://doi.org/10.33096/ilkom.v13i2.851.168-174
Perwitasari, A. S. (2021). Tumbuh 100%, Mola kini punya lebih dari 1 juta pelanggan berbayar di Indonesia. Kontan.Co.Id. https://industri.kontan.co.id/news/tumbuh-100-mola-kini-punya-lebih-dari-1-juta-pelanggan-berbayar-di-indonesia
Ramos, S., Soares, J., Cembranel, S. S., Tavares, I., Foroozandeh, Z., Vale, Z., & Fernandes, R. (2021). Data mining techniques for electricity customer characterization. Procedia Computer Science, 186, 475–488. https://doi.org/10.1016/j.procs.2021.04.168
Rizki, M., Umam, M. I. H., & Hamzah, M. L. (2020). Aplikasi Data Mining Dengan Metode CHAID Dalam Menentukan Status Kredit. SITEKIN: Jurnal Sains, Teknologi dan Industri, 18(1), 29-33.
Rinaldi, A., Rahmadani, N., Papilo, P., Silvia, S., & Rizki, M. (2021). Analisa Pengambilan Keputusan Pemilihan Bahan Dalam Pembuatan Kemeja Menggunakan Metode TOPSIS. SITEKIN: Jurnal Sains, Teknologi dan Industri, 18(2), 163-172.
Sarbaini, S., Cynthia, E. P., & Arifandy, M. I. (2021). Pengelompokan Diabetic Macular Edema Berbasis Citra Retina Mata Menggunakan Fuzzy Learning Vector Quantization (FLVQ). SITEKIN: Jurnal Sains, Teknologi dan Industri, 19(1), 75-80.
Saputra, S. A., Rosiyadi, D., Gata, W., & Husain, S. M. (2019). Sentiment Analysis Analysis of E-Wallet Sentiments on Google Play Using the Naive Bayes Algorithm Based on Particle Swarm Optimization. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 377–382. https://doi.org/10.29207/resti.v3i3.1118
Wahyudi, R., & Kusumawardana, G. (2021). Analisis Sentimen pada Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine. Jurnal Informatika, 8(2), 200–207. https://doi.org/10.31294/ji.v8i2.9681
Zuriel, H. P. P., & Fahrurozi, A. (2021). Implementasi Algoritma Klasifikasi Support Vector Machine Untuk Analisa Sentimen Pengguna Twitter Terhadap Kebijakan Psbb. Jurnal Ilmiah Informatika Komputer, 26(2), 149–162. https://doi.org/10.35760/ik.2021.v26i2.4289
Published
2022-04-14
How to Cite
Hendriyanto, M., Ridha, A. A., & Enri, U. (2022). Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine. INTECOMS: Journal of Information Technology and Computer Science, 5(1), 1-7. https://doi.org/https://doi.org/10.31539/intecoms.v5i1.3708
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