Support Vector Machine: Analisis Sentimen Aplikasi Saham di Google Play Store

Main Article Content

Sri Lestari
Sudin Saepudin

Abstract

The best application predicate has been awarded to the application that has the highest downloads and high star rating on Google Play Store. In rating an application, user comments need to be considered because many stock investment apps have almost the same downloads and star ratings, so the title of best app is a problem. Based on this condition, this research aims to analyze user feedback of stock investment applications as a variable to determine which stock investment application is the best on the Google Play Store. This study using the Support Vector Machine (SVM) classification method with the support of Rapid Miner to carry out the calculation process. From this research, it produces positive sentiment values for each application, namely HSB Investment about 1,134 positive sentiments with an accuracy value of 88.70%, Ajaib about 936 positive sentiments with an accuracy value of 61.89%, Pluang about 703 positive sentiments with an accuracy value of 68.25%, Bibit about 322 positive sentiments with an accuracy value of 64.89%, and Stockbit about 124 positive sentiments with an accuracy value of 66.95%. So it can be concluded that the HSB application as the best stock investment application based on user comments reviews where this application has the most positive sentiment reviews with a high accuracy value.

Article Details

How to Cite
Support Vector Machine: Analisis Sentimen Aplikasi Saham di Google Play Store. (2021). JUSIFO (Jurnal Sistem Informasi), 7(2), 81-90. https://doi.org/10.19109/jusifo.v7i2.9825
Section
Articles

How to Cite

Support Vector Machine: Analisis Sentimen Aplikasi Saham di Google Play Store. (2021). JUSIFO (Jurnal Sistem Informasi), 7(2), 81-90. https://doi.org/10.19109/jusifo.v7i2.9825

References

Ahmadi, M. I., Apriani, F., Kurniasari, M., Handayani, S., & Gustian, D. (2020). Sentiment Analysis Online Shop on The Play Store Using Method Support Vector Machine (SVM). Seminar Nasional Informatika (Semnasif), 196–203. http://jurnal.upnyk.ac.id/index.php/semnasif/article/view/4101

DeHaff, M. (2010). Sentiment Analysis, Hard But Worth It! Customerthink.Com. https://customerthink.com/sentiment_analysis_hard_but_worth_it/

Erfina, A., Basryah, E. S., Saepulrohman, A., & Lestari, D. (2020). Analisis Sentimen Aplikasi Pembelajaran Online di Play Store pada Masa Pandemi Covid-19 Menggunakan Algoritma Support Vector Machine (SVM). Seminar Nasional Informatika (SEMASIF 2020). http://jurnal.upnyk.ac.id/index.php/semnasif/article/view/4094

Fadilah, W. R. U., Agfiannisa, D., & Azhar, Y. (2020). Analisis Prediksi Harga Saham PT Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine. Fountain of Informatics Journal, 5(2), 45–51. https://doi.org/10.21111/FIJ.V5I2.4449

Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool, 5(1), 1–184. https://doi.org/10.2200/S00416ED1V01Y201204HLT016

Luqyana, W. A., Cholissodin, I., & Perdana, R. S. (2018). Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer2, 2(11), 4704–4713. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3051

Mujilahwati, S. (2016). Pre-Processing Text Mining Pada Data Twitter. Seminar Nasional Teknologi Informasi Dan Komunikasi, 2016(Sentika), 49–56.

Pravina, A. M., Cholissodin, I., & Adikara, P. P. (2019). Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM) | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(3), 2789–2797. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4793

Pynam, V., Spanadna, R. R., & Srikanth, K. (2018). An Extensive Study of Data Analysis Tools (Rapid Miner, Weka, R Tool, Knime, Orange). International Journal of Computer Science and Engineering, 5(9), 4–11. https://doi.org/10.14445/23488387/IJCSE-V5I9P102

Que, V. K. S., Iriani, A., & Purnomo, H. D. (2020). Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(2), 162–170. https://doi.org/10.22146/JNTETI.V9I2.102

Ramadani, F. (2016). Pengaruh Inflasi, Suku Bunga, dan Nilai Tukar Rupiah Terhadap Harga Saham Perusahaan Sektor Properti dan Real Estate yang Tercatat di Bursa Efek Indonesia. Manajemen Bisnis, 6(1), 72–82. https://doi.org/10.22219/JMB.V6I1.5392

Santoso, I., Gata, W., & Paryanti, A. B. (2019). Penggunaan Feature Selection di Algoritma Support Vector Machine untuk Sentimen Analisis Komisi Pemilihan Umum. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 364–370. https://doi.org/10.29207/RESTI.V3I3.1084

Santoso, V. I., Virginia, G., & Lukito, Y. (2017). Penerapan Sentiment Analysis pada Hasil Evaluasi Dosen dengan Metode Support Vector Machine. Jurnal Transformatika, 14(2), 79–83. https://doi.org/10.26623/TRANSFORMATIKA.V14I2.439

Tuhuteru, H., & Iriani, A. (2018). Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier. Jurnal Informatika: Jurnal Pengembangan IT, 3(3), 394–401. https://doi.org/10.30591/JPIT.V3I3.977

Yunita, N. (2016). Analisis Sentimen Berita Artis dengan Menggunakan Algoritma Support Vector Machine dan Particle Swarm Optimization. Jurnal Sistem Informasi, 5(2), 104–112. https://ejournal.antarbangsa.ac.id/jsi/article/view/110