aspg-3-2691-1791419682 20261008003442 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 2 M. Asyhraf Zamir Zamri FAC. of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, MALAYSIA Nurul Aswa Omar FAC. of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, MALAYSIA Isredza Rahmi A. Hamid FAC. of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, MALAYSIA Recently, the popularity of online games has risen drastically due to the latest technology that can connect players globally. League of Legends (LoL) holds the title of being the most extensively played Multiplayer Online Battle Arena (MOBA) game globally. This issue compels a substantial volume of preceding research that still analyzes and predicts the game outcomes with traditional methods that can be inaccurate and imprecise. Furthermore, these methods are frequently associated with the high rates of both false positive and false negative results. Hence, this paper presents a weighted-based feature predictor model to enhance the prediction accuracy. The approach predicts the game outcome of League of Legends matches in the Latin America North (LAN) and North America (NA) regions. We utilize player mastery and win rate for each summoner as the features. The data preparation process includes a weighted algorithm calculation and then evaluation using Naïve Bayes and Support Vector Machine algorithm. The outcomes illustrate that the weight-based feature approach can predict the outcome of LoL matches with an average accuracy of over 97 percent. This approach can be a valuable technique for players, teams, and coaches to analyze their performance and make strategic decisions. 2024 132 144 2691 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150212 https://www.americaspg.com/journal/3/article/2691 https://www.americaspg.com/storage/91713578989.pdf