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Muhammad Wahyu Nugroho Sakti
Achmad Basuki
Ali Ridho Barakbah

Abstract

Multi-player online games have a matchmaking system, where two or more players can meet each other in a virtual room [19][20]. Players do not gain competitive experience when the matchmaking system uses a single parameter because the system cannot recognize players properly. Previous studies carried out several matchmaking methods, namely skill-based, behavior-based, role-based, and latency-based. However, few studies have discussed the computation of multi-parameter aggregation. We propose a matchmaking system that combines three essential things that become competitive matchmaking parameters: skill rating, behavior cluster, and location cluster. The Elo rating system will use to measure player ability. It will combine with player behavior clustering and player location clustering. It combines a statistical approach to managing distributions and unsupervised machine learning to identify players. Grouping players with machine learning methods is a new thing that has been researched in recent years [6][8]. The application of the algorithm with these parameters is carried out on the trivia education game to make it more competitive

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How to Cite
Nugroho Sakti, M. W. . ., Basuki, A. . . and Barakbah, A. R. . . (2022) “Implementation of Elo Rating System and Player Clustering for Competitive Matchmaking in Trivia Education Game ”, Jurnal Mantik, 5(4). Available at: https://www.iocscience.org/ejournal/index.php/mantik/article/view/1886 (Accessed: 21February2024).
References
[1] C. Bauckhage, A. Drachen, C. Thurau, “The Age of Analytics”. IEEE Transactions on Computational Intelligence and AI in Games, vol.7, no. 3, pp. 205–206, 2015.
[2] Badan Ekonomi Kreatif (BEKRAF), “Buku Aspek Kelayakan Pembiayaan Ekonomi Kreatif Subsektor Game”, 2017.
[3] Indonesian Internet Service Provider Association, Jurnal “Buletin APJII”, Edition 22 March 2018, p.3, 2018.
[4] Indonesian Internet Service Provider Association, “Survey Report APJII 2019-2020 (Q2)”, 2020.
[5] A. Drachen, C. Thurau, J. Togelius, G.N. Yannakakis, “Game Analytics : Maximizing the Value of Player Data”, Chapter 12 : Game Datamining. Springer, pp. 205-253, 2013.
[6] P. Braun, A. Cuzzocrea, T. D. Keding, C. K. Leung, A. G. M. Padzor, D. Sayson, “Game Data Mining: Clustering and Visualization of Online Game Data in Cyber-Physical Worlds”. Procedia Computer Science, no 112, pp. 2259–2268, 2017.
[7] K. Anagnostou, M. Maragoudakis, “Data Mining for Player Modelling in Videogames”, 13th Panhellenic Conference on Informatics, 2009.
[8] C. Bauckhage, A. Drachen, R. Sifa, “Clustering Game Behavior Data”. IEEE Transactions on Computational Intelligence and AI in Games”, vol. 7, no. 3, pp. 266–278, 2015.
[9] S. C. J. Bakkes, P. H. M. Spronck, G. van Lankveld, “Player behavioural modelling for video games”. Entertainment Computing, vol. 3, no. 3, 71–79, 2012.
[10] H. Wang, H.-T. Yang, C.-T. Sun, “Thinking Style and Team Competition Game Performance and Enjoyment”. IEEE Transactions on Computational Intelligence and AI in Games, vol. 7 No. 3, 243–254, 2015.
[11] Gow, J., Baumgarten, R., Cairns, P., Colton, S., & Miller, P, “Unsupervised Modeling of Player Style With LDA”. IEEE Transactions on Computational Intelligence and AI in Games, 4(3), 152–166, 2012.
[12] O. Delalleau, E. Contal, E. Thibodeau-Laufer, R. C. Ferrari, Y. Bengio, F. Zhang, “Beyond Skill Rating: Advanced Matchmaking in Ghost Recon Online”, IEEE Transactions on Computational Intelligence and AI in Games, 4(3), 167–177, 2012.
[13] R.T. Fielding, R.N. Taylor, “Architectural Styles and the Design of Network-based Software Architectures”, Doctor of Philosophy Dissertation, University of California, 2000.
[14] V. Pimentel, B. G. Nickerson, “Communicating and Displaying Real-Time Data with WebSocket”. IEEE Internet Computing, vol. 16 no. 4, pp. 45–53, 2012.
[15] Pelánek, R, “Applications of the elo rating system in adaptive educational systems”. Comput. Educ. 98, 169–179 (2016).
[16] A. Amany, “Quizizz sebagai Media Evaluasi Pembelajaran Daring Pelajaran Matematika”, Jurnal Buletin Pengembangan Perangkat Pembelajaran , Vol.2 No.2, p. 48-57, 2020.
[17] Thabtah, F., Padmavathy, A. J., & Pritchard, A. (2020). “Chess Results Analysis Using Elo Measure with Machine Learning”. Journal of Information & Knowledge Management, 2020.
[18] Kovalchik, S, “Extension of the Elo rating system to margin of victory”, International Journal of Forecasting, 36(4), 1329–1341, 2020.
[19] Pramono, M.F., Renalda, K., Kristiadi, D.P., Warnars, H.L.H.S. and Kusakunniran, W, “Matchmaking problems in MOBA Games”. Int Journal of Electrical Engineering and Computer Science,11(3), 2018.
[20] Tsai, F. H., “The Effectiveness Evaluation among Different Player-Matching Mechanisms in a Multi-Player Quiz Game”, Educational Technology & Society, 19 (4), 213–224, 2016.
[21] Wardaszko, M., ?wil, M., Chojecki, P., & D?browski, K., “Analysis of matchmaking optimization systems potential in mobile eSports”, 52nd Hawaii International Conference on System Sciences, 2019.
[22] Mishan, M. T., Fadzil, A. F. A., Samah, K. A. F. A., Baharin, N. F., & Anuar, N., “Business Intelligence for Paintball Tournament Matchmaking Using Particle Swarm Optimization”. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), pp. 599-606, 2018.
[23] Hajas, C. and Zempléni, A., “Chess and bridge: clustering the countries”, Annales Univ. Sci. Budapest., Sect. Comp. 46, 67–79, 2017.
[24] A. R. Barakbah, K. Arai, “Identifying moving variance to make automatic clustering for normal data set”, IECI Japan Workshop, Musashi Institute of Technology, Tokyo, 2004.
[25] A. R. Barakbah, K. Arai, “Determining constraints of moving variance to find global optimum and make automatic clustering”, Proc. Industrial Electronics Seminar (IES), pp. 409-413, 2004.
[26] A. R. Barakbah, K. Arai, “Reversed pattern of moving variance for accelerating automatic clustering”, EEPIS journal, vol. 9, no. 2, pp.15-21, 2004.
[27] M. Boro?, J. Brzezi?ski, A.Kobusi?ska, “P2P matchmaking solution for online games”, Peer-to-peer networking and applications, Vol. s12083-019-00725-3, pp 1–14, 2019.
[28] M. Mylak, D. Deja, “Developing game-structure sensitive matchmaking system for massive-multiplayer online games”, Social Informatics, Springer, pp. 200–208, 2014.
[29] S. Agarwal and J. R. Lorch, “Matchmaking for online games and other latency-sensitive P2P systems”, Proc. ACM SIGCOMM Conf. Data Commun., pp. 315–326, 2009.
[30] A. H. Christiansen, B. F. Nielsen, and E. Gensby, “Multi-parameterised matchmaking: A framework”, IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2018, pp. 377–380.
[31] J. Manweiler, S. Agarwal, M. Zhang, R. Roy Choudhury, and P. Bahl, “Switchboard: a matchmaking system for multiplayer mobile games”. Proceedings of the 9th international conference on Mobile systems, applications, and services, MobiSys ’11, pages 71–84, 2011.
[32] Z. Chen, S. Xue, J. Kolen, N. Aghdaie, K. A. Zaman, Y. Sun, and M. Seif El-Nasr, “EOMM: An engagement optimized matchmaking framework,” Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 1143–1150, 2017
[33] K. Arai, A.R. Barakbah, “Hierarchical K-means: an algorithm for centroids initialization for K-means”, Reports of the Faculty of Science and Engineering, Saga University, Japan, Vol. 36, No. 1, 2007.
[34] Machine Learning for Optimal Matchmaking (2020) YouTube video, added by GDC [Online]. Available at https://www.youtube.com/watch?v=Q8BX0nXfPjY [Accessed 11 October 2021]