Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier

Authors

  • M Ikhsan Maulana Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru
  • Elvia Budianita Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru
  • Muhammad Fikry Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru
  • Febi Yanto Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru

DOI:

https://doi.org/10.30865/json.v4i3.5854

Keywords:

Game, Battle Royale, Sausage Man, Sentiment Analysis, Classification

Abstract

Battle Royale games are games that mix adventure and survival elements with last man standing game modes. One of the most popular battle royale games is the Sausage Man game. The number of complaints such as bugs, cheaters, and FPS which continues to decrease makes the game annoying. The solution is that developers must improve and improve game security so that users feel comfortable playing the game. There are many opinions or reviews from users regarding problems in the game, sentiment analysis will be carried out on the Sausage Man application review data on the Google play store as a process to produce categorization of opinions through reviews. The purpose of the researcher is to carry out a sentiment analysis to see positive, neutral or negative opinions from Sausage Man game users. The stages carried out in this study were data collection using web scraping, data labeling, text preprocessing, document weighting, classification, and evaluation. The results of data labeling using the VADER Lexicon obtained 1089 reviews (36.3%) for positive sentiment, 912 reviews for neutral sentiment (30.4%), and 999 reviews for negative sentiment (33.3%). Classification using the Naïve Bayes Classifier. Evaluation using the Confusion Matrix by dividing 90% training data and 10% test data produces an accuracy of 75%, 79% precision, and 75% recall. For the division of 80% training data 20% of the test data produces an accuracy of 73%, 76% precision and 73% recall. Positive sentences are found more often, but the accuracy is still below 80%.

References

Y. Asri, W. N. Suliyanti, D. Kuswardani, dan M. Fajri, “Pelabelan Otomatis Lexicon Vader dan Klasifikasi Naive Bayes dalam menganalisis sentimen data ulasan PLN Mobile,†vol. 15, no. 2, hal. 264–275, 2022.

J. Watori, R. Aryanti, dan A. Junaidi, “Penggunaan Algoritma Klasifikasi Terhadap Analisa Sentimen Pemindahan Ibukota Dengan Pelabelan Otomatis,†J. Inform., vol. 7, no. 1, hal. 85–90, 2020.

R. Y. Lesmana dan R. Andarsyah, “Model Klasifikasi Multinomial Naive Bayes Untuk Analisis Sentiment Terkait Non-Fungible Token,†J. Tek. Inform., vol. 14, no. 3, hal. 135–139, 2022.

S. Pamungkas dan J. B. B. Darmawan, “Klasifikasi Sentiment Tweet Pelanggan IndiHome Selama Pandemi Covid-19 Menggunakan Algoritma Multinomial Naive Bayes,†SNESTIK, hal. 339–344, 2022.

M. G. T. Akbar dan D. B. Srisulistiowati, “Analisa Sentimen Efektifitas Vaksin terhadap Varian COVID 19 Omicron Berbasis Leksikon,†J. Inf. Inf. Secur., vol. 2, no. 2, hal. 251–258, 2021.

E. A. Marwa dan A. B. Kristanto, “Analisis Sentimen Pengungkapan Informasi Manajemen : Text Mining Berbasis Metode VADER,†Own. Ris. J. Akunt., vol. 6, hal. 2973–2984, 2022.

W. Parasati, F. A. Bachtiar, dan N. Y. Setiawan, “Analisis Sentimen Berbasis Aspek pada Ulasan Pelanggan Restoran Bakso President Malang dengan Metode Naive Bayes Classifier,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 4, hal. 1090–1099, 2020.

N. S. Wardani, A. Prahutama, dan P. Kartikasari, “Analisis Sentimen Pemindahan Ibu Kota Negara dengan Klasifikasi Naive Bayes untuk Model Bernoulli dan Multinomial,†J. Gaussian, vol. 9, hal. 237–246, 2020.

G. Sanjaya dan K. M. Lhaksmana, “Analisis Sentimen Komentar YouTube tentang Terpilihnya Menteri Kabinet Indonesia Maju Menggunakan Lexicon Based,†e-Proceeding Eng., vol. 7, no. 3, hal. 9698–9710, 2020.

L. Ardiani, H. Sujaini, dan Tursina, “Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan di Kota Pontianak,†J. Sist. dan Teknol. Inf., vol. 8, no. 2, hal. 183–190, 2020, doi: 10.26418/justin.v8i2.36776.

N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, dan Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store menggunakan Naive Bayes dan Support Vector Machine,†J. Comput. Eng. Syst. Sci., vol. 5, no. 2, hal. 293–298, 2020.

A. Deolika, Kusrini, dan E. T. Luthfi, “Analisis Pembobotan Kata pada Klasifikasi Text Mining,†J. Teknol. Inf., vol. 3, no. 2, hal. 179–184, 2019.

M. R. Fahlevvi, “Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia di Google Playstore Menggunakan Metode Support Vector Machine,†J. Teknol. dan Komun. Pemerintah., vol. 4, no. 1, hal. 1–13, 2022.

R. A. R. Wiguna dan A. I. Rifai, “Analisis Text Clustering Masyarakat Di Twitter Mengenai Omnibus Law Menggunakan Orange Data Mining,†J. Inf. Syst. Informatics, vol. 3, no. 1, hal. 1–12, 2021.

E. Fitri, Y. Yuliani, S. Rosyida, dan W. Gata, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes , Random Forest Dan Support Vector Machine,†TRANSFORMTIKA, vol. 18, no. 1, hal. 71–80, 2020.

D. N. Chandra, G. Indrawan, dan I. N. Sukajaya, “Klasifikasi Berita Lokal Radar Malang Menggunakan Metode Naive Bayes dengan Fitur N-Gram,†J. Ilmu Komput. Indones., vol. 4, no. 2, 2019.

O. S. D. Silaen, Herlawati, dan Rasim, “Analisis Sentimen Mengenai Gangguan Bipolar Pada Twitter Menggunakan Algoritma Naive Bayes,†J. Komtika (Komputasi dan Inform., vol. 6, no. 2, hal. 63–73, 2022.

T. T. Widowati, “Analisis Sentimen Twitter terhadap Tokoh Republik dengan Algoritma Naive Bayes dan Support Vector Machine,†J. SIMETRIS, vol. 11, no. 2, 2020.

Downloads

Published

2023-03-31

How to Cite

Maulana, M. I., Budianita, E., Fikry, M., & Yanto, F. (2023). Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier. Jurnal Sistem Komputer Dan Informatika (JSON), 4(3), 485–492. https://doi.org/10.30865/json.v4i3.5854

Issue

Vol. 4 No. 3 (2023): Maret 2023

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).