Win Probability of Heroes in Mobile Legends MPL ID S12 Competitions Using Nae Bayes Algorithm

 (*)Angga Permana Putra Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Pulung Nurtantio Andono (Universitas Dian Nuswantoro, Semarang, Indonesia)

(*) Corresponding Author

Submitted: December 18, 2023; Published: January 10, 2024


The development of the gaming industry into digital formats has become a rapidly growing trend. E-Sports, particularly in Indonesia, has shown significant growth alongside technological advancements. The increased interest in E-Sports is evidenced by the higher quality tournaments organized by local game developers, such as Moonton, a subsidiary of ByteDance, hosting tournaments for Mobile Legends: Bang Bang. This article aims to analyze the probability of winning heroes in the Mobile Legends Professional League Season 12 using the Naive Bayes algorithm. The results of calculating the probabilities for various hero roles show varying levels of winning potential. By utilizing this method, it becomes possible to predict hero victories or losses more systematically, aiding players in developing more effective strategies during matches. The results obtained from predicting hero victories and losses indicate that for the jungler role, the win rate is 0.145 and the loss rate is 0.088. For midlaners, the victory rate reaches 0.492 and 0.661 for losses. As for roamers, the win rate is 0.120 and the loss rate is 0.102. For goldlaners and explaners, they achieve win rates of 0.528 and 0.177, respectively, while their loss rates are 0.339 and 0.132. Furthermore, after testing the data, the accuracy obtained for the roles is as follows: jungler role 67.61%, midlaner role 67.5%, roamer 67.65%, goldlaner 67.29%, and explaner 67.71%.


Classification; Hero; Mobile Legends Game; MPL; Naive Bayes

Full Text:


Article Metrics

Abstract view : 196 times
PDF - 59 times


R. Rinaldi and I. Krisnadi, Analisa Dampak Perkembangan Esports Terhadap Persaingan Operator Seluler Di Indonesia, Progr. Stud. Magister Tek. Elektro, Fak. Tek. Univ. Mercubuana, no. 1, pp. 16, 2019.

A. Tatya Admaja and Y. A. Saputro, Perkembangan E-Sport Pada Pelajar Remaja Usia 13-16 Tahun Pada Masa Pandemi Covid-19, J. Phys. Heal. Recreat., vol. 2, no. 1, pp. 6973, 2021.

D. C. Quartino and F. A. Irawan, Analisis Perkembangan E-Sport Ditinjau Dari Revolusi Industri 4.0 di Kota Semarang Analysis of the Development of E-Sport reviewed From The Industrial Revolution 4.0 in the Semarang, J. Literasi Olahraga, vol. 2, no. 3, pp. 169173, 2022, [Online]. Available:

A. Z. Rosyidi and Suparlan, REGISTER BAHASA KOMENTATOR MOBILE LEGENDS DALAM TURNAMEN MPL SEASON 5, J. Penelit. Dan Ilmu Pendidik., vol. 2, no. 2, pp. 174182, 2021, doi: 10.51620/0869-2084-2021-66-8-465-471.

H. P. Jiwandono and E. R. Purwandi, Liga Seluler: pergeseran olahraga elektronik ke peranti seluler di Indonesia., J. Media dan Komun. Indones., vol. 1, no. 1, p. 4, 2020, doi: 10.22146/jmki.51164.

W. F. A and Moch. F. N, KOMUNIKASI, KOORDINASI, DAN KERJASAMA DALAM GAME KOMPETITIF MOBILE LEGEND, J. Ilm. Indones., vol. 7, no. 5, pp. 55115525, 2022, [Online]. Available:

D. Irwandi, R. Masykur, and S. Suherman, Korelasi Kecanduan Mobile Legends Terhadap Prestasi Belajar Mahasiswa, J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 2, no. 3, pp. 292299, 2021, doi: 10.46306/lb.v2i3.87.

J. Mantik et al., Sentimen Analisis Hero Mobile Legends Dengan Algoritma Naive Bayes, J. Mantik, vol. 6, no. 3, pp. 26854236, 2022.

L. M. I. Fajri, Y. P, I. M. Z, W. T, and L. E. Rahmawati, PERILAKU BERBAHASA YOUTUBER GAMING MOBILE LEGEND, vol. 6, no. 1, pp. 8694, 2022, [Online]. Available:

A. Nasrullah and Sewaka, Perancangan Sistem Informasi E-Sports Di Indonesia ( Khususnya Mobile Legends ) Berbasis Website Menggunakan Metode OOAD ( Object Oriented Analysis Design ), J. Ilmu Komput. dan Sience, vol. 1, no. 5, p. 499, 2022.

R. Haditira et al., Analisis Sentimen Pada Steam Review Menggunakan Metode Multinomial Nave Bayes dengan Seleksi Fitur Gini Index Text, e-Proceeding Eng., vol. 9, no. 3, pp. 17931799, 2022, [Online]. Available:

T. Astuti and Y. Astuti, Analisis Sentimen Review Produk Skincare Dengan Nave Bayes Classifier Berbasis Particle Swarm Optimization (PSO), J. Media Inform. Budidarma, vol. 6, no. 4, p. 1806, 2022, doi: 10.30865/mib.v6i4.4119.

D. Kusnanda and A. Permana, Implementation of Naive Bayes Classifier (NBC) for Sentiment Analysis on Twitter in Mobile Legends, Int. J. Sci. Technol. Manag., vol. 4, no. 5, pp. 11321138, 2023.

A. S. Chan, F. Fachrizal, and A. R. Lubis, Outcome Prediction Using Nave Bayes Algorithm in the Selection of Role Hero Mobile Legend, in Journal of Physics: Conference Series, Institute of Physics Publishing, Jul. 2020, pp. 16. doi: 10.1088/1742-6596/1566/1/012041.

N. Rahma, Hubungan Konformitas Teman Sebaya Dengan Intensitas Bermain Game Online Mobile Legends, Psikoborneo J. Ilm. Psikol., vol. 10, no. 2, p. 281, 2022, doi: 10.30872/psikoborneo.v10i2.7385.

A. R. Dikananda, I. Ali, Fathurrohman, R. Ade Rinaldi, and Iin, Genre e-sport gaming tournament classification using machine learning technique based on decision tree, Nave Bayes, and random forest algorithm, IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012037, 2021, doi: 10.1088/1757-899x/1088/1/012037.

A. K. S. Ong et al., Determination of Factors Influencing the Behavioral Intention to Play Mobile Legends: Bang-Bang during the COVID-19 Pandemic: Integrating UTAUT2 and System Usability Scale for a Sustainable E-Sport Business, Sustain., vol. 15, no. 4, pp. 126, 2023, doi: 10.3390/su15043170.

S. M. Listijo, T. Purwani, S. T. Galih, and T. Hafidzin, Prediksi Kemenangan Dan Susunan Tim Pada Game Mobile Legends Bang Bang Menggunakan Algoritma Nave Bayes, Komputaki , vol. 6, no. 1, pp. 1517, 2020.

K. Akhmedov and A. H. Phan, Machine learning models for DOTA 2 outcomes prediction, pp. 111, 2021, [Online]. Available:

N. Salmi and Z. Rustam, Nave Bayes Classifier Models for Predicting the Colon Cancer, IOP Conf. Ser. Mater. Sci. Eng., vol. 546, no. 5, 2019, doi: 10.1088/1757-899X/546/5/052068.

K. Khadijah, N. Sabilly, and F. A. Nugroho, Sentiment Analysis of League of Legends: Wild Rift Reviews on Google Play Using Na Ve Bayes Classifier, J. Ilm. Kursor, vol. 12, no. 1, pp. 2330, 2023, [Online]. Available:

R. Ardianto, T. Rivanie, Y. Alkhalifi, F. S. Nugraha, and W. Gata, Sentiment Analysis on E-Sports for Education Curriculum Using Naive Bayes and Support Vector Machine, J. Ilmu Komput. dan Inf., vol. 13, no. 2, pp. 109122, 2020, doi: 10.21609/jiki.v13i2.885.

M. K. Mayangsari, I. Syarif, and A. Barakbah, Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification, Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, pp. 277288, 2023, doi: 10.22219/kinetik.v8i3.1740.

N. Asmiati and Fatmawati, Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Pengaruh Negatif Game Online Bagi Remaja Milenial, JTIM J. Teknol. Inf. dan Multimed., vol. 2, no. 3, pp. 141149, 2020, doi: 10.35746/jtim.v2i3.102.

A. P. Wibawa et al., Nave Bayes Classifier for Journal Quartile Classification, Int. J. Recent Contrib. from Eng. Sci. IT, vol. 7, no. 2, p. 91, 2019, doi: 10.3991/ijes.v7i2.10659.

M. F. Sarifah, Naive bayes algorithm performance for smartphone sentiment analysis in social media, Int. J. Artif. Intell. Informatics, vol. 2, no. 2, pp. 98107, 2022, doi: 10.33292/ijarlit.v2i2.41.

V. Jackins, S. Vimal, M. Kaliappan, and M. Y. Lee, AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes, J. Supercomput., vol. 77, no. 5, pp. 51985219, 2021, doi: 10.1007/s11227-020-03481-x.

M. Alehegn, R. R. Joshi, and P. Mulay, Diabetes analysis and prediction using random forest, KNN, Nave Bayes, and J48: An ensemble approach, Int. J. Sci. Technol. Res., vol. 8, no. 9, pp. 13461354, 2019.

K. Lemons, A Comparison Between Nave Bayes and Random Forest to Predict Breast Cancer IJURCA : International Journal of Undergraduate Research & Creative Activities A Comparison Between Nave Bayes and Random Forest to Predict, vol. 12, no. 1, 2023.

M. Guia, R. R. Silva, and J. Bernardino, Comparison of Naive Bayes, support vector machine, decision trees and random forest on sentiment analysis, IC3K 2019 - Proc. 11th Int. Jt. Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag., vol. 1, no. Ic3k, pp. 525531, 2019, doi: 10.5220/0008364105250531.

N. T. M. Hazmiza, Classifying Violent Elements in Role-Playing Games Based on User Review using Nave Bayes Technique, Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 1.3, pp. 402407, 2020, doi: 10.30534/ijatcse/2020/6391.32020.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Win Probability of Heroes in Mobile Legends MPL ID S12 Competitions Using Na´ve Bayes Algorithm


  • There are currently no refbacks.


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

STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998

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