Penerapan Metode Random Forest untuk Prediksi Win Ratio Pemain Player Unknown Battleground

Authors

  • Reinardus Aji Haristu Universitas Sanata Dharma Yogyakarta
  • Paulina H. Prima Rosa Universitas Sanata Dharma Yogyakarta

DOI:

https://doi.org/10.54367/means.v4i2.545

Keywords:

data mining, Classification, Random Forest Method, Player Unknown Battleground, PUBG

Abstract

Online game that is on the rise as one of e-sports is the unknown battleground player. To win the game requires the right strategy. In this paper, a study of effective playing strategies in the player unknown battleground game is explored by extracting data from player statistics taken from the kaggle website. From the statistical data, a model is made to predict the win ratio of each player using the random forest method. From the results of the study, random forest was able to produce an accuracy of 88.19%.

References

S. M. Jannah, “Bukan Cuma Main Game, Esport Mulai Jadi Industri Masa Depan,†2018. [Online]. Available: https://finance.detik.com/berita-ekonomi-bisnis/d-4316768/bukan-cuma-main-game-esport-mulai-jadi-industri-masa-depan. [Accessed: 24-Oct-2019].

R. Restika, “Apa Itu Esports? - Esportsnesia,†2018. [Online]. Available: https://esportsnesia.com/penting/apa-itu-esports/. [Accessed: 24-Oct-2019].

A. Suryo, “UniPin Bikin Kompetisi eSports Berhadiah Rp 1,4 Miliar,†2018. [Online]. Available: https://inet.detik.com/games-news/d-4162385/unipin-bikin-kompetisi-esports-berhadiah-rp-14-miliar. [Accessed: 24-Oct-2019].

O. Laoly and T. Limbong, “Visualisasi Pengumuman dan SOP Fakultas Ilmu Komputer Universitas Katolik Santo Thomas Medan berbasis Multimedia,†MEANS (Media Inf. Anal. dan Sist., vol. 3, no. 2, pp. 126–139, Dec. 2018.

W. O. Nidhomuddin;Bambang, “RANDOM FOREST DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) BINARY RESPONSE UNTUK KLASIFIKASI PENDERITA HIV/AIDS DI SURABAYA | Nidhomuddin | Jurnal Statistika Universitas Muhammadiyah Semarang,†Jurnal Statistika Universitas Muhammadiyah Semarang, 3, 1, 2015, 2015. [Online]. Available: https://jurnal.unimus.ac.id/index.php/statistik/article/view/1439. [Accessed: 24-Oct-2019].

Yusuf Sulistyo Nugroho; Nova Emiliyawati, “Sistem Klasifikasi Variabel Tingkat Penerimaan Konsumen Terhadap Mobil Menggunakan Metode Random Forest,†2017. [Online]. Available: https://www.researchgate.net/publication/320413581_Sistem_Klasifikasi_Variabel_Tingkat_Penerimaan_Konsumen_Terhadap_Mobil_Menggunakan_Metode_Random_Forest. [Accessed: 24-Oct-2019].

J. Han, Data mining: Data mining concepts and techniques. 2014.

L. Breiman, “Random Forests. transparencias,†Statistics (Ber)., vol. 45, no. 1, pp. 1–33, 2001.

S. Polamuri, “How the random forest algorithm works in machine learning,†2017. [Online]. Available: https://dataaspirant.com/2017/05/22/random-forest-algorithm-machine-learing/. [Accessed: 24-Oct-2019].

Published

2019-10-24

How to Cite

Haristu, R. A., & Rosa, P. H. P. (2019). Penerapan Metode Random Forest untuk Prediksi Win Ratio Pemain Player Unknown Battleground. MEANS (Media Informasi Analisa Dan Sistem), 4(2), 120–128. https://doi.org/10.54367/means.v4i2.545

Issue

Section

Daftar Artikel