MOBA GAME REVIEW SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM

Alif Fajar Panjalu, Syariful Alam, Mochammad Imam Sulistyo

Abstract


MOBA as one of the many popular subgenres today, Mobile Legend, Arena of Valor, League of Legend and Lokapala based on the large number of downloads can be mentioned as popular MOBA games, but the rating of these four applications is below 4.0 on the Google Play platform Store, this happens because some users may think that this mobile MOBA game has several advantages, but also some disadvantages that affect ratings. This study aims to determine the results of sentiment analysis on mobile MOBA games using Google Play Store reviews. Then it is processed using Python programming to create a model with the linear kernel Support Vector Machine (SVM) algorithm to classify the dataset. From the results of the classification model test using 19,579 data, where there were 10,017 positive sentiment data and 9,562 negative sentiment data and the distribution of train data and test data was 70%: 30%, obtained an accuracy of 82.64% and then re-evaluated using the Cross Validation method using 5 times so that an accuracy of 83.38% is obtained

 

Keywords: MOBA, Sentiment Analysis, Machine Learning, SVM, Cross Validation


Full Text:

PDF

References


D. Sinaga and C. Jatmoko, “ANALISIS SENTIMEN UNTUK MENGETAHUI KESAN PLAYER GAME MOBILE LEGENDS MENGGUNAKAN NAÃVE BAYES CLASSIFIER SENTIMENT ANALYSIS TO KNOW IMPRESSIONS PLAYER GAME MOBILE LEGENDS USING NAÃVE BAYES CLASSIFIER 1),†Semnas Lppm, pp. 540–547, 2020, [Online]. Available: www.netlytic.org

A. Nurzahputra and A. Muslim, “Analisis Sentimen pada Opini Mahasiswa Menggunakan Natural Language Processing,†Seminar Nasional Ilmu Komputer, pp. 114–118, Oct. 2016.

H. Simorangkir and K. M. Lhaksmana, “Analisis Sentimen pada Twitter untuk Games Online Mobile Legends dan Arena of Valor dengan Metode Naïve Bayes Classifier,†eProceedings of Engineering, vol. Vol. 5, no. No. 3, pp. 8131–8140, Dec. 2018, [Online]. Available: https://dev.twitter.com.

S. Fachri Pane and J. Ramdan, “Pemodelan Machine Learning : Analisis Sentimen Masyarakat Terhadap Kebijakan PPKM Menggunakan Data Twitter,†Jurnal Sistem Cerdas, vol. Vol.05, no. No.01, pp. 12–20, 2022, [Online]. Available: https://t.co/IEnucGFuuJ, doi: https://doi.org/10.37396/jsc.v5i1.191.

A. Adjie Wicaksono, R. Yusuf, T. Aristi Saputri, and S. Dharma Wacana, “PENERAPAN NATURAL LANGUAGE PROCESSING BERBASIS VIRTUAL ASSISTANT PADA BAGIAN ADMINISTRASI AKADEMIK STMIK DHARMA WACANA,†IRobot, vol. Vol.05, pp. 33–47, 2021, doi: https://doi.org/10.53514/ir.v5i1.228.

W. Widayani and H. Harliana, “Perbandingan Kernel Support Vector Machine Dalam Melakukan Klasifikasi Penundaan Biaya Kuliah Mahasiswa,†Jurnal Sains dan Informatika, vol. 7, no. 1, pp. 20–27, Jun. 2021, doi: 10.34128/jsi.v7i1.268,doi: https://doi.org/10.34128/jsi.v7i1.268.

A. Nabillah, S. Alam, and M. G. Resmi, “Twitter User Sentiment Analysis Of TIX ID Applications Using Support Vector Machine Algorithm,†2022.

R. Nanda, E. Haerani, S. K. Gusti, and S. Ramadhani, “Klasifikasi Berita Menggunakan Metode Support Vector Machine,†Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 5, no. 2, 2022, doi: https://doi.org/10.32672/jnkti.v5i2.4193

G. R. Ditami, E. F. Ripanti, and H. Sujaini, “Implementasi Support Vector Machineuntuk Analisis Sentimen Terhadap Pengaruh Program Promosi EventBelanja pada Marketplace,†JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. Vol.8, no. No.3, pp. 508–516, Dec. 2022, doi: https://doi.org/10.26418/jp.v8i3.56478.

D. Darwis, E. Shintya Pratiwi, A. Ferico, and O. Pasaribu, “PENERAPAN ALGORITMA SVM UNTUK ANALISIS SENTIMEN PADA DATA TWITTER KOMISI PEMBERANTASAN KORUPSI REPUBLIK INDONESIA,†2020, doi: https://doi.org/10.21107/edutic.v7i1.8779

A. Zaiem Praghakusma and N. Charibaldi, “Komparasi Fungsi Kernel Metode Support Vector Machine untuk Analisis Sentimen Instagram dan Twitter (Studi Kasus : Komisi Pemberantasan Korupsi),†vol. 9, no. 2, pp. 33–42, 2021, doi: 10.12928/jstie.v8i3.xxx.

S. C. Nurzanah, S. Alam, and T. I. Hermanto, “ANALISIS ASSOCIATION RULE UNTUK IDENTIFIKASI POLA GEJALA PENYAKIT HIPERTENSI MENGGUNAKAN ALGORITMA APRIORI (STUDI KASUS: KLINIK RAFINA MEDICAL CENTER),†Jurnal Informatika dan Komputer) Akreditasi KEMENRISTEKDIKTI, vol. 5, no. 2, 2022, doi: 10.33387/jiko.

F. Romadoni, Y. Umaidah, and B. N. Sari, “Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine,†Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 9, no. 2, pp. 247–253, Jul. 2020, doi: 10.32736/sisfokom.v9i2.903.

D. Hana Amalia and W. Yustanti, “Klasifikasi Buku Menggunakan Metode Support Vector Machine pada Digital Library,†Journal of Informatics and Computer Science, vol. 03, 2021, [Online]. Available: https://opac.unesa.ac.id/, doi: https://doi.org/10.26740/jinacs.v3n01.p55-61

H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,†ILKOM Jurnal Ilmiah, vol. 12, no. 2, pp. 81–86, Aug. 2020, doi: 10.33096/ilkom.v12i2.507.81-86, doi: https://doi.org/10.33096/ilkom.v12i2.507.81-86




DOI: https://doi.org/10.33387/jiko.v6i2.6388

Refbacks

  • There are currently no refbacks.