Implementation of Naïve Bayes in M-Series 4 Mobile Legends for Winning Prediction
DOI:
https://doi.org/10.37859/coscitech.v5i1.6707
Abstract
Mobile Legends is a game made by a developer from China called Moontoon which implements the Multiplayer Online Battle Arena (MOBA) system which is currently popular. The popularity of this game is proven by the holding of low, middle and high level tournaments. Recently a high level or international tournament called the M-Series World Championship was held in Indonesia. This game is played by two teams consisting of five players with the aim of destroying enemy targets in the form of towers. The problem in this game is winning and losing. One of the factors that determines victory or defeat is the choice of hero. The wrong hero composition during the draft pick stage can make it difficult for your team to play and lead to unexpected results. This research aims to predict the percentage level of Mobile Legends wins based on the drafted heroes. Prediction is the process of minimizing errors in systematically estimating the future based on past information. The technique used in this research is the Naïve Bayes algorithm. The Naïve Bayes algorithm is a classification method based on probability. This method consists of four stages, namely data understanding, data preparation, data analysis, and results analysis. This research dataset is provided by Youtube MPL Indonesia. The dataset consists of 880 training data and 90 test data for M-Series 4 Mobile Legends. The results of this research provide a percentage value in the form of prediction of 96.67%, precision of 95.65% and recall of 97.78%. The results of an accuracy rate of 96.67% using the Naïve Bayes algorithm show that predictions using the Naïve Bayes algorithm can be applied to predict win ratios in M-Series 4 Mobile Legends.
Downloads
References
[2] E. Ramadanti dan M. Muslih, “Penerapan Data Mining Algoritma K-Means Clustering Pada Populasi Ayam Petelur Di Indonesia,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 7, no. 1, hal. 1–7, 2022, doi: 10.36341/rabit.v7i1.2155.
[3] R. H. Sukarna dan Y. Ansori, “Implementasi Data Mining Menggunakan Metode Naive Bayes Dengan Feature Selection Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” J. Ilm. Sains dan Teknol., vol. 6, no. 1, hal. 50–61, 2022, doi: 10.47080/saintek.v6i1.1467.
[4] M. Yudhi Putra dan D. Ismiyana Putri, “Pemanfaatan Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Klasifikasi Jurusan Siswa Kelas XI,” J. TEKNO KOMPAK, vol. 16, no. 2, hal. 176–187, 2022.
[5] S. Bayulianto, I. Purnamasari, dan M. Jajuli, “Prediksi Tingkat Kemenangan Mobile Legends Profesional League Indonesia Season 9 Dengan Menggunakan Algoritma Naïve Bayes,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 8, no. 2, hal. 538–550, 2023, doi: 10.29100/jipi.v8i2.3562.
[6] I. Harifal et al., “SISTEMASI: Jurnal Sistem Informasi Optimasi Naïve Bayes dengan PSO untuk Prediksi Kebutuhan ICU Pasien Covid-19 Naive Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients,” vol. 11, no. September, hal. 724–734, 2022, [Daring]. Tersedia pada: http://sistemasi.ftik.unisi.ac.id
[7] R. Hendra Tinambunan, J. Titaley, dan C. E. Mongi, “Klasifikasi Naive Bayes Dalam Memprediksi Tingkat Program Studi Matematika Fmipa Universitas Sam Ratulangi Manado,” Pros. Semin. Nas. Sains Dan Terap. Vi, no. April, hal. 141–149, 2022.
[8] Veratiwi dan Dafid, “PREDIKSI KASUS COVID-19 INDONESIA MENGGUNAKAN METODE NAÏVE BAYES PREDICTING INDONESIAN ’ S COVID -19 CASES USING NAÏVE BAYES METHOD Universitas Multi Data Palembang , Sumatera Selatan , Indonesia Poltekkes Kemenkes Palembang , Sumatera Selatan , Indonesia,” J. Kesehat. Poltekkes Palembang, vol. 17, no. 2, hal. 1–7, 2022.
[9] F. Apri Wenando, R. Hayami, S. Soni, A. Fitria, dan D. Shifana, “Sentimen Analisis Masyarakt terhadap Kasus Penembakan Brigadir J Menggunakan Algoritma Naïve Bayes Classifier,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 2, hal. 484–490, 2023, doi: 10.37859/coscitech.v4i2.5686.
[10] H. Mukhtar, J. Al Amien, dan M. A. Rucyat, “Filtering Spam Email menggunakan Algoritma Naïve Bayes,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 1, hal. 9–19, 2022, doi: 10.37859/coscitech.v3i1.3652.
[11] R. Hayami, Soni, dan I. Gunawan, “Klasifikasi Jamur Menggunakan Algoritma Naïve Bayes,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 1, hal. 28–33, 2022, doi: 10.37859/coscitech.v3i1.3685.
