Prediksi Kemenangan eSport DOTA 2 Berdasarkan Data Pertandingan
Submitted : 2020-01-20, Published : 2020-01-23.
Abstract
DOTA 2 is one of the eSports that are in great demand both by the general society and the game professional communities. They compete with each other to develop the best strategy to defeat all enemies they faced. In order to develop the best strategy, a good and accurate analysis system is needed. Data mining can be used to solve these problems by digging valuable information from dataset using certain method. Prediction method is one of the methods in data mining that is most appropriate for finding the winning predictions for the DOTA 2 game. One method that is quite simple and can be used is Naive Bayes. The results of this study indicate that Naive Bayes can make predictions well with an accuracy of 98,804 %. The data used in this research as much as 50000 that obtained from open data. It is expected that this research can assist players in providing information for developing game strategies.
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Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques 3rd Edition. Morgan Kaufmann.
Kusrini dan Luthfi, E. T. (2009). Algoritma Data Mining. Andi.
Pratiwi, R. W., & Nugroho, Y. S. (2016). Prediksi Rating Film Menggunakan Metode Naïve Bayes. Jurnal Teknik Elektro, 8(2), 59-63.
Guntur, M., Santony, J., & Yuhandri. (2018). Prediksi Harga Emas dengan Menggunakan Metode Naïve Bayes dalam Investasi untuk Meminimalisasi Resiko. Jurnal RESTI : Rekayasa Sistem dan Teknologi Informasi, 2(1), 354-360.
Syarli & Muin, A. A., (2016). Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi). Jurnal Ilmiah Ilmu Komputer, 2(1), 22-26.
Anzelmo, D. (Diperbaharui pada November 2019). Dota 2 Matches. https://www.kaggle.com/devinanzelmo/dota-2-matches. Diakses pada tanggal 3 Januari 2020.
Prasetyo, E. (2012). Data Mining: Konsep dan Aplikasi menggunakan Matlab. Andi.
Weka versi 3.8.4. Diunduh dari https://www.cs.waikato.ac.nz/~ml/weka/.
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