MOBILE LEGEND GAME PREDICTION USING MACHINE LEARNING REGRESSION METHOD

I Gede Wiarta Sena, Andi W. R. Emanuel

Abstract


Abstract: A research institute explains that with 83.7 million people using the Internet, Indonesia is among the top 20 internet users globally. Various individual or group activities require an internet network, one of which is playing games, for developments in the gaming sector, especially the MOBA (Massive Online Battle Arena) genre game, is being hotly discussed. There are various kinds of MOBA genre games, one of which is the Mobile Legends game. Many E-Sport Mobile Legends teams, especially in Asia, make this phenomenon a business space to generate large profits. In this study, the researcher recommends a good machine learning algorithm to predict the outcome of Mobile Legends matches. Of the 600 match history data analyzed, this study recommends the Artificial Neural Network (ANN) and Random Forest (RF) algorithms as the right algorithms to predict the outcome of the match. Prediction results from each algorithm can reach 82% and 80% accuracy. These findings can help the E-sports analysis team build their match strategy.

           
Keywords: artificial neural networ; machine learning; mobile legend; prediction; random forest

 

 

Abstrak: Sebuah lembaga penelitian menjelaskan bahwa dengan 83,7 juta penduduk yang menggunakan Internet, Indonesia termasuk di dalam 20 besar pengguna internet secara global. Berbagai aktivitas individu atau kelompok membutuhkan jaringan internet, salah satunya adalah bermain game, untuk perkembangan pada sektor game khususnya game bergenre MOBA (Massive Online Battle Arena) sedang hangat diperbincangkan. Ada berbagai macam game bergenre MOBA, salah satunya game Mobile Legends. Banyak tim E-Sport Mobile Legends khususnya di asia menjadikan fenomena ini sebagai ruang bisnis untuk menghasilkan keuntungnya yang besar. Dalam penelitian ini, peneliti merekomendasikan algoritma pembelajaran mesin yang baik untuk memprediksi hasil pertandingan Mobile Legends. Dari 600 data riwayat pertandingan yang dianalisis, penelitian ini merekomendasikan algoritma Artificial Neural Network (ANN) dan Random Forest (RF) sebagai algoritma yang tepat untuk memprediksi hasil pertandingan. Hasil prediksi dari masing-masing algoritma dapat mencapai 82% dan akurasi 80%. Temuan ini dapat membantu tim analisis E-sports membangun strategi pertandingan mereka.

 

Kata kunci: artificial neural network; machine learning; mobile legend; prediksi; random forest

 


Full Text:

PDF

References


A. Katona, R. Spick, V. J. Hodge, S. Demediuk, F. Blok, A. Drachen, J. A. Walker, "Time to Die: Death Prediction in Dota 2 Using Deep Learning," Conf. Proc. - IEEE Conference on Computatonal Intelligence and Games, CIGKatona, A., Spick, 2019, doi: 10.1109/CIG.2019.8847997.

A. S. Chan, F. Fachrizal, and A. R. Lubis, "Outcome Prediction Using Naïve Bayes Algorithm in the Selection of Role Hero Mobile Legend," J. Phys. Conf. Ser., vol. 1566, no. 1, 2020, doi: 10.1088/1742-6596/1566/1/012041.

U. Tokac, E. Novak, and C. G. Thompson, “Effects of game-based learning on students’ mathematics achievement: A meta-analysis,” J. Comput. Assist. Learn., vol. 35, no. 3, pp. 407–420, 2019, doi: 10.1111/jcal.12347.

L. Dahabiyeh, M. S. Najjar, and D. Agrawal, “The effect of risk levels on technology adoption decision: the case of online games,” Inf. Technol. People, vol. 33, no. 5, pp. 1445–1464, 2020, doi: 10.1108/ITP-09-2019-0455.

Z. Yu, M. Gao, and L. Wang, “The Effect of Educational Games on Learning Outcomes, Student Motivation, Engagement and Satisfaction,” J. Educ. Comput. Res., vol. 59, no. 3, pp. 522–546, 2021,doi:10.1177/0735633120969214.

M. Mora-Cantallops and M. Á. Sicilia, "MOBA games: A literature review," Entertain. Comput., vol. 26, no. February, pp. 128–138, 2018, doi: 10.1016/j.entcom.2018.02.005.

J. M. Zhang, M. Harman, L. Ma, and Y. Liu, "Machine Learning Testing: Survey, Landscapes and Horizons," IEEE Trans. Softw. Eng., pp. 1–1, 2020, doi: 10.1109/tse.2019.2962027.

V. J. Hodge, S. Devlin, N. Sephton, F. Block, P. I. Cowling, and A. Drachen, "Win Prediction in Multiplayer Esports: Live Professional Match Prediction," IEEE Trans. Games, vol. 13, no. 4, pp. 368–379, 2021, doi: 10.1109/TG.2019.2948469.

K. Passi and N. Pandey, "Increased Prediction Accuracy in the Game of Cricket Using Machine Learning," Int. J. Data Min. Knowl. Manag. Process, vol. 8, no. 2, pp. 19–36, 2018, doi: 10.5121/ijdkp.2018.8203.

J. Le Louedec, T. Guntz, J. L. Crowley, and D. Vaufreydaz, "Deep learning investigation for chess player attention prediction using eye-tracking and game data," Eye Track. Res. Appl. Symp., 2019, doi: 10.1145/3314111.3319827.

V. J. Hodge, S. Devlin, N. Sephton, F. Block, P. I. Cowling, and A. Drachen, “Win Prediction in Multiplayer Esports: Live Professional Match Prediction,” IEEE Trans. Games, vol. 13, no. 4, pp. 368–379, 2021, doi: 10.1109/TG.2019.2948469.

D. Gourdeau and L. Archambault, “Discriminative Neural Network for Hero Selection in Professional Heroes of the Storm and DOTA 2,” IEEE Trans. Games, vol. 13, no. 4, pp. 380–387, 2021, doi: 10.1109/TG.2020.2972463.

M. Aung et al., "Predicting Skill Learning in a Large, Longitudinal MOBA Dataset," IEEE Conf. Comput. Intell. Games, CIG, vol. 2018-August, pp. 1–7, 2018, doi: 10.1109/CIG.2018.8490431.

K. Akhmedov and A. H. Phan, "Machine learning models for DOTA 2 outcomes prediction," pp. 1–11, 2021, [Online]. Available: http://arxiv.org/abs/2106.01782.

J. P. Lai, Y. M. Chang, C. H. Chen, and P. F. Pai, “A survey of machine learning models in renewable energy predictions,” Appl. Sci., vol. 10, no. 17, 2020, doi: 10.3390/app10175975.

J. P. Lai, Y. M. Chang, C. H. Chen, and P. F. Pai, “A survey of machine learning models in renewable energy predictions,” Appl. Sci., vol. 10, no. 17, 2020, doi: 10.3390/app10175975.

W. Gu, K. Foster, J. Shang, and L. Wei, "A game-predicting expert system using big data and machine learning," Expert Syst. Appl., vol. 130, pp. 293–305, 2019, doi: 10.1016/j.eswa.2019.04.025.

R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 56–70, 2020, doi: 10.38094/jastt1224.

J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70–79, 2018, doi: 10.1016/j.neucom.2017.11.077.

T. F. Johnson, N. J. B. Isaac, A. Paviolo, and M. González-Suárez, “Handling missing values in trait data,” Glob. Ecol. Biogeogr., vol. 30, no. 1, pp. 51–62, 2021, doi: 10.1111/geb.13185.

S. A. Zahin, C. F. Ahmed, and T. Alam, "An effective method for classification with missing values," Appl. Intell., vol. 48, no. 10, pp. 3209–3230, 2018, doi: 10.1007/s10489-018-1139-9.

J. Kiani, C. Camp, and S. Pezeshk, "On the application of machine learning techniques to derive seismic fragility curves," Comput. Struct., vol. 218, no. xxxx, pp. 108–122, 2019, doi: 10.1016/j.compstruc.2019.03.004.

V. Romeo et al., "Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa," Magn. Reson. Imaging, vol. 64, pp. 71–76, 2019, doi: 10.1016/j.mri.2019.05.017.

F. Jia, Y. Lei, N. Lu, and S. Xing, "Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization," Mech. Syst. Signal Process., vol. 110, pp. 349–367, 2018, doi: 10.1016/j.ymssp.2018.03.025.

H. Yang et al., "Immune-Related Prognostic Model in Colon Cancer: A Gene Expression-Based Study," Front. Genet., vol. 11, no. May, pp. 1–10, 2020, doi: 10.3389/fgene.2020.00401.

S. Gupta, R. Gupta, M. Ojha, and K. P. Singh, "A comparative analysis of various regularization techniques to solve overfitting problem in artificial neural network," Commun. Comput. Inf. Sci., vol. 799, pp. 363–371, 2018, doi: 10.1007/978-981-10-8527-7_30.

I. Santos, L. Castro, N. Rodriguez-Fernandez, Á. Torrente-Patiño, and A. Carballal, Artificial Neural Networks and Deep Learning in the Visual Arts: a review, vol. 33, no. 1. 2021. doi: 10.1007/s00521-020-05565-4.

T. Wisanwanichthan and M. Thammawichai, “A Double-Layered Hybrid Approach for Network Intrusion Detection System Using Combined Naive Bayes and SVM,” IEEE Access, vol. 9, pp. 138432–138450, 2021, doi: 10.1109/ACCESS.2021.3118573.

W. Muhammad, M. Mushtaq, K. N. Junejo, and M. Y. Khan, "Sentiment analysis of product reviews in the absence of labelled data using supervised learning approaches," Malaysian J. Comput. Sci., vol. 33, no. 2, pp. 118–132, 2020, doi: 10.22452/mjcs.vol33no2.3.

M. Bouazizi and T. Ohtsuki, "Multi-class sentiment analysis on twitter: Classification performance and challenges," Big Data Min. Anal., vol. 2, no. 3, pp. 181–194, 2019, doi: 10.26599/BDMA.2019.9020002.




DOI: https://doi.org/10.33330/jurteksi.v9i2.1866

Article Metrics

Abstract view : 859 times
PDF - 1135 times

Refbacks

  • There are currently no refbacks.


Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK ROYAL 

Copyright © LPPM STMIK ROYAL

 

Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.