Win Probability of Heroes in Mobile Legends MPL ID S12 Competitions Using Naïve Bayes Algorithm
Angga Permana Putra*, Pulung Nurtantio Andono
Fakultas Ilmu Komputer, Teknik Informatika, Universitas Dian Nuswantoro, Semarang, Indonesia Email: 1,*[email protected], 2[email protected]
Correspondence Author Email: [email protected]
Abstract−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%.
Keywords: Classification; Hero; Mobile Legends Game; MPL; Naive Bayes
1. INTRODUCTION
The advancement of technology has transformed the face of the gaming industry into a rapidly evolving digital format [1]. The rapid growth of e-Sports is closely tied to the advancement of increasingly sophisticated technology in this era [2]. The emergence of various e-Sports competitions in Indonesia is evidence of the increasing interest in e-Sports [3]. E-Sport is an electronic sports branch supported by IESPA (Indonesia E-Sport Association) [4].
What sets it apart is that eSports athletes do not compete physically, but rather focus more on strategy, with matches taking place online through computers, enabling each team to compete without face-to-face interaction [5]. With the official recognition of IeSPA status by the government, it provides greater assurance to them that this hobby will receive further support from the government [6]. One of the game types that has gained recognition among the public is the MOBA game genre, and Mobile Legends is one of the popular MOBA games at present. Mobile Legends: Bang Bang is an online game developed and published by Moonton, a subsidiary of ByteDance [7]. This game features two teams consisting of five members each, engaging in real-time battles, with an average match duration ranging between 10 to 20 minutes [8]. The team that successfully destroys the opponent's main tower will emerge as the winner [9]. After achieving success, Moonton ventured into the world of esports by organizing several regional tournaments named Mobile Legends Bang Bang Professional League (MPL) [10].
This journal will focus on the win and loss probabilities of heroes used in the Mobile Legends Professional League Season 12 competition and the efficiency of strategies employed for a team to achieve a higher probability of winning during matches. The algorithm applied will be the Naive Bayes algorithm, recognized as one of the most efficient and effective inductive learning algorithms in the field of machine learning and data mining[11].
The Naive Bayes Classifier method has been chosen for its ease of implementation, computational speed, and high accuracy [12]. While the independence of attributes in actual data is rare [13], violating this assumption of attribute independence does not significantly impact the performance of the Naïve Bayes classification, which remains relatively high [14].
The relationship between Mobile Legends and Naive Bayes might have a strong correlation in the context of data analysis. Through data analysis conducted on Mobile Legends gameplay, Naive Bayes can be employed to analyze hero data, statistics, or patterns of wins and losses within the game [15]. By leveraging historical data collected from a vast number of previous matches, the Naive Bayes algorithm has the capability to form and develop a predictive model that can estimate the probability of a team's victory or defeat. This model takes into account various factors such as the combination of heroes used, relevant statistics, and distinctive patterns identified from previous data [16].
Related Works : The selection of heroes in MOBA (Multiplayer Online Battle Arena) games has emerged as a critical aspect that can significantly influence match outcomes [17]. Previous studies have explored various analysis and prediction techniques, including the utilization of the Naive Bayes classification method, to comprehend patterns in hero selection within similar gaming environments [18].
In 2021, Akhmedov and Anh Huy conducted an in-depth study exploring player profiling modeling within the realm of Multiplayer Online Battle Arena (MOBA) games, particularly focusing on the renowned game Dota 2 [19]. Throughout their research, they applied and assessed the effectiveness of the Naive Bayes classification method. Their findings notably revealed that the model developed using the Naive Bayes approach exhibited
substantial capability in accurately predicting players' preferences for hero selection based on their previously documented gameplay patterns [20].
A separate research endeavor conducted in 2020 by Khadijah,K. Sabilly, N. And Nugroho, F A. delved into the comprehensive analysis of gameplay dynamics within the mobile gaming sensation, Mobile Legends.
Furthermore, their study aimed at the development of an advanced hero recommendation system tailored for this game[21]. Employing the Naive Bayes approach, they systematically evaluated user preferences and devised a mechanism to offer hero recommendations based on the intricate patterns derived from players' previous gaming sessions. This approach aimed to enhance user experience by providing personalized hero suggestions aligned with individual gameplay styles and preferences, leveraging insights gleaned from the Naive Bayes methodology applied to Mobile Legends' gameplay data [22].
Their comprehensive study was characterized by a multifaceted approach that encompassed not only the analysis of raw gameplay data but also the nuanced understanding of user interactions within the gaming ecosystem [23]. By leveraging the Naive Bayes methodology, they meticulously scrutinized and evaluated the intricate nuances of user preferences, systematically discerning patterns that emerged from players' previous gaming sessions [24]. This detailed analysis sought to capture the underlying subtleties and recurrent patterns embedded within the gameplay dynamics of Mobile Legends [25]. Furthermore, their research endeavor aspired to transcend the conventional boundaries of recommendation systems by focusing on a more personalized approach. The primary aim was to create an intuitive mechanism capable of delivering tailored hero recommendations based on an individual player's gaming history and strategic inclinations [26]. By amalgamating the insights derived from the Naive Bayes methodology[27] with the rich repository of Mobile Legends' gameplay data, the envisioned system aimed to augment user experience by offering astute and personalized hero suggestions, thereby providing players with strategic guidance closely aligned with their individual gameplay styles and preferences.
2. RESEARCH METHODOLOGY
2.1 Research Stages
This research aims to focus on the probability of winning and losing associated with the heroes used in the Mobile Legends Bang Bang Professional League Season 12 and the efficiency of strategies employed by the participating teams in this competition. The data utilized in this study is sourced from the entire outcomes of Mobile Legends Bang Bang Professional League Season 12, encompassing both the group stage and playoff rounds,Also sourced from the official Moonton website and the Mobile Legends game itself, as well as from YouTube channels such as MPL INDONESIA and KBGGWP. The methodology adopted for this research involves classification utilizing the Naive Bayes algorithm. Naive Bayes can be employed to analyze hero data, statistics, or win-loss patterns within Mobile Legends Bang Bang games. Leveraging a comprehensive historical dataset derived from a multitude of previous matches during the Mobile Legends Bang Bang Professional League Season 12 competition, the Naive Bayes algorithm possesses the capability to formulate and develop a predictive model proficient in estimating the likelihood of a team's victory or defeat. Mobile Legends and Naive Bayes may exhibit a close correlation within the context of data analysis. Through the data analysis conducted on Mobile Legends gameplay, Naive Bayes can be utilized to analyze hero data, statistics, or patterns of wins and losses within Mobile Legends matches. Naive Bayes is chosen due to its ease of implementation, computational speed, and high accuracy. While the actual data may seldom exhibit attribute independence, violating this assumption of attribute independence does not significantly impact the classification performance of Naïve Bayes, which remains sufficiently high. To see more clearly in Figure 1, which is the stages of the research.
Figure 1. Flowchart. Data Analysis Process 2.2 Algoritma Naïve Bayes
The Naive Bayes Classifier algorithm relies on Bayesian Theorem and is often employed when dealing with high- dimensional input.[28] This is one of the useful techniques in statistically classifying documents, enabling the
prediction of class membership probabilities from given data. In the fields of machine learning and data mining, Naive Bayes has proven to be one of the highly effective and efficient inductive learning algorithms.[29]
Independence of attributes in actual data is rarely observed; however, despite this, assuming attribute independence can lead to relatively high performance in Naïve Bayes classification. The probability p (C = ci | X
= xj) indicates the likelihood of attribute Xi with value xi given class c, where in Naive Bayes, class C is qualitative, whereas attribute Xi can be qualitative or quantitative. If attribute Xi is quantitative, the probability of occurrence p (X = xi | C = cj) will be very small, rendering the equality of probabilities unreliable for issues concerning quantitative attribute types. Therefore, to address quantitative attributes, several approaches can be employed, such as using a normal distribution [30].
F = N(Xi; μc, Oe) =1−(X1−μc)2
√2μcOe (1)
Numeric values will be mapped to values in interval form; the Naïve Bayes computation is illustrated as follows [31].
ρ(I = ij | C = cj) =p(I=Ij) p(C=ci | I=ij)
P(C=Ci) (2)
Explanation: p(I=ij|C=ci) : interval opportunies i-j for ci class p(C=ci|I=ij) : ci class opportuniees for i-j interval p(I=ij) : Probability interval of -j on all formed interval p(C=ci) : chance of an -i class for all classes in the dataset
The data obtained from MPL Season 12 consists of 80 matches, comprising 208 matches overall, with 72 matches during the Regular Season and 8 matches in the Play-offs. A total of 80 heroes were utilized throughout this tournament. Prediction Include :
1) Hero Type refers to the classification of heroes based on their utilization of either close-range or long-range weapons.
2) Hp Hero represents the total health power of heroes categorized by thick or thin life points.
3) Mana Hero denotes the overall power utilized by heroes to deploy their abilities.
4) Defend Hero showcases the cumulative defense possessed by heroes, categorized as high or low.
5) Attack Hero reflects the total attack statistic possessed by heroes, classified as high or low.
3. RESULT AND DISCUSSION
These findings were obtained from a series of matches within the Mobile Legends Bang Bang Professional League Season 12, involving 9 teams: ONIC, GEEK FAM, BIGETRON, RRQ, DEWA UNITED, REBELLION ZION, EVOS LEGENDS, ALTER EGO, and AURA FIRE. The competition comprised 80 matches during MPL Season 12, consisting of 72 matches in the regular season and 8 matches in the playoff round. In total, 208 games took place throughout the Mobile Legends Bang Bang Professional League Season 12 tournament. In this tournament, a total of 80 heroes were utilized across various matches conducted throughout the competition.
Upon establishing the attributes of the Mobile Legends heroes, the subsequent step involved assigning ID ratings to each attribute, facilitating ease in computations using the Naive Bayes algorithm. The Naive Bayes Classifier method was selected due to its implementation simplicity, computational speed, and high accuracy.
Despite the infrequent occurrence of attribute independence in actual data, violating this assumption of attribute independence does not significantly impact the Naïve Bayes classification performance, which remains notably high. The subsequent step involves prediction based on the existing dataset. Fact for probability:
Data = 416
P(Y = WIN) = 208/416 = 0.5 P(Y = LOSE) = 208/416 = 0.5 Fact:
P(JUNGLER|Y = WIN) = 208/416 = 0.5 P(JUNGLER|Y = LOSE) = 208/416 = 0.5
Perform all the same calculations. Following the statistical analysis of the 416 data points, the subsequent step involves generating a dataset for training using the 416 data based on the previously acquired data.
Prediction Include :
1) Hero Type refers to the classification of heroes based on their utilization of either close-range or long-range weapons.
2) Hp Hero represents the total health power of heroes categorized by thick or thin life points.
3) Mana Hero denotes the overall power utilized by heroes to deploy their abilities.
4) Defend Hero showcases the cumulative defense possessed by heroes, categorized as high or low.
5) Attack Hero reflects the total attack statistic possessed by heroes, classified as high or low.
Training data :
"The next prediction based on the dataset utilizes the Naive Bayes algorithm."
ρ(Y|X) =p(X|Y)p(Y)
P(X) (3)
Next, identify the rating ID and the attributes held by the dataset as shown in the table 1.
Table 1. Attribute mobile legend id_atribut name_atribut stat_atribut
1 TYPE Avaiable
2 HP Avaiable
3 MANA Avaiable
4 ATTACK Avaiable
5 DEFEND Avaiable
6 RESULT Find??
After assigning an ID rating to each attribute in table 1, the next step involves calculating the probabilities for each role. There are 5 roles for which probabilities will be calculated (Jungler, Midlaner, Roamer, Goldlaner, and Explaner) using the existing attributes, including hero type, hero HP, hero mana, hero attack, and hero defense.
This calculation is performed across all data using the same formula. And put it in table 2 Table 2. Hero Prediction
ROLE TYPE HP MANA ATTACK DEFEND RESULT
JUNGLER MELEE FAT LOW LOW HIGH ??
MIDLANER RANGE THIN HIGH LOW HIGH ??
ROAMER MELEE FAT LOW HIGH HIGH ??
GOLDLANER RANGE THIN LOW LOW LOW ??
EXPLANER MELEE FAT LOW LOW HIGH ??
3.1 Looking for probabilities
Calculating the probability of each role with different attributes.
1) Jungler 2) Midlaner 3) Roamer 4) Goldlaner 5) Explaner
Calculating Precision, Recall, and Accuracy
1) Precision measures the accuracy of positive predictions generated by a model. It calculates the percentage of true positive results out of the total predicted positive results by the model.
TP
TP+FP (4)
2) Recall measures the model's ability to retrieve all true positive instances. It calculates the percentage of true positive results correctly identified by the model from the entire pool of true positive instances.
TP
TP+FN (5)
3) Accuracy measures how well a model correctly predicts both positive and negative outcomes. It quantifies the percentage of correct predictions made by the model in relation to the overall predictions.
TP+TN
TP+TN+FP+FN (6)
These three metrics are crucial to understanding how well a classification model performs in predicting outcomes overall and its ability to accurately identify positive results.
The first role to calculate its probability is the jungler role, characterized by the attributes of being melee- type, having high health points (FAT), low mana (LOW), low attack (LOW), and high defense (HIGH).
Table 3. Hero Prediction 1 (jungler) Atribut Poin Atribut Result
TYPE MELEE WIN 208 208 1
MELEE LOSE 207 208 0.9951
HP FAT WIN 146 208 0.7019
FAT LOSE 125 208 0.6009
MANA LOW WIN 120 208 0.5769
LOW LOSE 120 208 0.5769
ATTACK LOW WIN 51 208 0.2451
LOW LOSE 41 208 0.1971
DEFEND HIGH WIN 147 208 0.7067
HIGH LOSE 131 208 0.6298
RESULT WIN 0.0701381 208 0.145887 RESULT LOSE 0,0428211 208 0,088906
Calculating probabilities using the hero role "jungler" with attributes of melee type, having thick health (FAT), low mana (LOW), low attack (LOW), and high defense (HIGH). Based on the calculations performed for the hero role "jungler" with attributes of melee type, thick health (FAT), low mana (LOW), low attack (LOW), and high defense (HIGH), in table 3 the results yielded a percentage of 0.088 for losses and 0.145 for wins. The accuracy level attained after the completion of calculations in the table 3 stands at 67.61%.
Precition : 0.6818 or equal to 68.18%
Recall : 0.7692 or equal to 76.92%
Accuracy : 0.6761 or equal to 67.61%
The precision obtained from the table 3 is 68.18%, and the recall is 76.92%. Furthermore, the calculated accuracy stands at 67.61%. Next, calculate the probability of the hero role being a midlaner with attributes: ranged type, thin HP (THIN), high mana (HIGH), low attack (LOW), and high defense (HIGH).
Table 4. Hero Prediction 2 (Midlaner) Atribut Poin Atribut Result
TYPE RANGE WIN 207 208 0.9951
RANGE LOSE 202 208 0.9711
HP THIN WIN 129 208 0.6201
THIN LOSE 115 208 0.5528
MANA HIGH WIN 206 208 0.9903
HIGH LOSE 207 208 0.9951
ATTACK LOW WIN 207 208 0.9951
LOW LOSE 203 208 0.9759
DEFEND HIGH WIN 81 208 0.3894
HIGH LOSE 127 208 0.6105
RESULT WIN 0.2367870 208 0.492517 RESULT LOSE 0,3182656 208 0.661992
Calculating probabilities using the midlaner hero role with the attributes of ranged type, thin HP, high mana, low attack, and high defense. Based on the results of the calculations for heroes with the midlaner role possessing ranged type, thin HP, high mana, low attack, and high defense attributes, the obtained percentages from the table 4 reveal a 0.661 probability for losses and 0.492 for victories. This calculation yields an accuracy level of 67.5%.
Precision : 0.675 or equal to 67,5%
Recall : 0.607 or equal to 60,7%
Accuracy : 0.675 or equal to 67.5%
The precision obtained from table 4 is 67.5%, with a recall of 60.7%, and the calculated accuracy stands at 67.5%. Next, calculating the probability for the roamer hero role with attributes of being melee type, high HP (FAT), low mana (LOW), high attack (HIGH), and high defense (HIGH).
Table 5. Hero Prediction 3 (Roamer) Atribut Poin Atribut Result
TYPE MELEE WIN 152 208 0,7307
MELEE LOSE 149 208 0.7163
HP FAT WIN 130 208 0.625
FAT LOSE 128 208 0.6153
MANA LOW WIN 114 208 0.5480
LOW LOSE 135 208 0.6490
ATTACK HIGH WIN 97 208 0.4663
HIGH LOSE 83 208 0.3990
DEFEND HIGH WIN 103 208 0.4951
HIGH LOSE 90 208 0.4326
RESULT WIN 0.0577774 208 0.120177 RRESULT LOSE 0,0493725 208 0.102695
Calculating the probability of a roamer hero role with melee type, high health points (FAT), low mana (LOW), high attack (HIGH), and high defense (HIGH). Based on the results derived from the calculation of a roamer hero with melee type, high health points (FAT), low mana (LOW), low attack (HIGH), and high defense
(HIGH). After the computations were completed using the table 5, a win rate of 0.120 and a loss rate of 0.102 were obtained. The accuracy level reached 67.65%.
Precision : 0.6757 or equal to 67.57%
Recall : 0.7143 or equal to 71.43%
Accuracy : 0.6765 or equal to 67.65%
The precision obtained from table 5 is 67.57%, the recall is 71.43%, and the calculated accuracy stands at 67.65%. Next, calculate the probability of the Goldlaner hero role with attributes such as ranged type, thin HP (THIN), low mana (LOW), low attack (LOW), and low defense (LOW).
Table 6. Hero Prediction 4 (Goldlaner) Atribut Poin Atribut Result
TYPE RANGE WIN 208 208 1
RANGE LOSE 205 208 0.9855
HP THIN WIN 199 208 0.9567
THIN LOSE 197 208 0.9471
MANA LOW WIN 102 208 0.4903
LOW LOSE 72 208 0.3461
ATTACK LOW WIN 165 208 0.7932
LOW LOSE 163 208 0.7836
DEFEND LOW WIN 142 208 0.6826
LOW LOSE 134 208 0.6442
RESULT WIN 0.2539724 208 0.528262 RESULT LOSE 0.1630681 208 0.339181
Calculating the probability of a goldlaner hero role with characteristics including ranged type, low health (THIN), low mana (LOW), low attack (LOW), and low defense (LOW). Based on the computed probabilities for a goldlaner hero role with ranged type, low health (THIN), low mana (LOW), low attack (LOW), and low defense (LOW) in table 6, the resulting analysis yields a 0.339 percentage for losses and 0.528 for wins. Post-computation, the accuracy rate stands at 67.29%.
Precision : 0.6728 or equal to 67.28%
Recall : 0.7622 or equal to 76.22%
Accuracy : 0.6729 or equal to 67.29%
The precision obtained from table 6 is 67.28%, the recall is 76.22%, and the calculated accuracy stands at 67.29%. Lastly, calculating the probability of a hero with the role of explaner having attributes such as melee type, thick HP (FAT), low mana (LOW), high attack (HIGH), and high defense (HIGH).
Table 7. Hero Prediction 5 (Explaner) Atribut Poin Atribut Result
TYP MELEE WIN 208 208 1
MELEE LOSE 208 208 1
HP FAT WIN 156 208 0.75
FAT LOSE 135 208 0.6490
MANA LOW WIN 117 208 0.5625
LOW LOSE 108 208 0.5192
ATTACK LOW WIN 85 208 0.4086
LOW LOSE 91 208 0.4375
DEFEND HIGH WIN 103 208 0.4951
HIGH LOSE 90 208 0.4326
RESULT WIN 0.0853444 208 0.177516 RESULT LOSE 0.0637740 208 0.132650
Calculating the probability of the explaner hero role with attributes of melee type, thick HP (FAT), low mana (LOW), high attack (HIGH), and high defense (HIGH). Based on the probability calculation results for the explaner hero role with attributes of melee type, thick HP (FAT), low mana (LOW), high attack (HIGH), and high defense (HIGH). Following the completion of calculations in the table 7, a percentage of 0.132 for losses and 0.177 for victories is obtained. The accuracy rate stands at 67.71%.
Precision : 0.6727 or equal to 67.27%
Recall : 0.74 or equal to 74%
Accuracy : 0.6771 or equal to 67.71%
The precision obtained from the table 7 is 67.27%, the recall is 74%, and the accuracy calculated from the computations stands at 67.71%, as seen in figures 2 and 3.
Figure 2. Probability of Winning
Figure 3. Accuration
The obtained calculations for win and loss predictions in the Mobile Legends Bang Bang Professional League Season 12 based on the Goldlaner hero role with attributes of ranged type, low HP (THIN), low mana (LOW), low attack (LOW), and low defense (LOW) indicate the highest prediction rate for wins. Conversely, for the roamer hero role with attributes of melee type, high HP (FAT), low mana (LOW), high attack (HIGH), and high defense (HIGH), it demonstrates the lowest prediction rate for victories.
4. CONCLUSION
The conclusion drawn from the analysis of win rate prediction for Mobile Legends heroes during MPL Season 12 tournaments using the Naïve Bayes method proves to be reliably consistent with previously established estimations. This method not only allows for accurate predictions but also enables the anticipation of hero victories or losses during the Mobile Legends Professional League Season 12 competitions. Implementing the Naïve Bayes algorithm facilitates systematic win rate predictions for Mobile Legends matches based on available data. Such predictive capabilities aid players in strategizing and training heroes that might have lower win rates, thereby making opponents find it challenging to predict the player's gaming strategies. The analysis results exhibit varying win rates and losses across different hero roles: jungler heroes showcase a win rate of 0.145 and a loss rate of 0.088, midlaner heroes demonstrate a win percentage of 0.492 with a higher loss rate reaching 0.661. Meanwhile, for roamer heroes, their win rate stands at 0.120, coupled with a loss rate of 0.102. Additionally, goldlaner and explaner heroes respectively display win rates of 0.528 and 0.177, with corresponding loss rates of 0.339 and 0.132. The accuracy of the tested data illustrates the prediction reliability across various hero roles, with the accuracy rates for jungler, midlaner, roamer, goldlaner, and explaner reaching 67.61%, 67.5%, 67.65%, 67.29%, and 67.71%, respectively.
REFERENCES
[1] 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. 1–6, 2019.
[2] 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. 69–73, 2021.
[3] 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. 169–173, 2022, [Online]. Available: https://journal.unsika.ac.id/index.php/JLO
[4] 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. 174–182, 2021, doi: 10.51620/0869-2084-2021-66- 8-465-471.
0 0,2 0,4 0,6 0,8
Jungler Midlaner Roamer Goldlaner Explaner
win Lose
0 0,2 0,4 0,6 0,8 1
Jungler Midlaner Roamer Goldlaner Explaner
Presisi Recall Akurasi
[5] 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.
[6] W. F. A and Moch. F. N, “KOMUNIKASI, KOORDINASI, DAN KERJASAMA DALAM GAME KOMPETITIF MOBILE LEGEND,” J. Ilm. Indones., vol. 7, no. 5, pp. 5511–5525, 2022, [Online]. Available:
https://jurnal.syntaxliterate.co.id/index.php/syntax-literate/article/view/7034/4379
[7] 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. 292–299, 2021, doi: 10.46306/lb.v2i3.87.
[8] J. Mantik et al., “Sentimen Analisis Hero Mobile Legends Dengan Algoritma Naive Bayes,” J. Mantik, vol. 6, no. 3, pp.
2685–4236, 2022.
[9] 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. 86–94, 2022, [Online]. Available: https://jurnal.unigal.ac.id/literasi/article/view/6753/4832 [10] 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.
[11] R. Haditira et al., “Analisis Sentimen Pada Steam Review Menggunakan Metode Multinomial Naïve Bayes dengan Seleksi Fitur Gini Index Text,” e-Proceeding Eng., vol. 9, no. 3, pp. 1793–1799, 2022, [Online]. Available:
https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/17982
[12] T. Astuti and Y. Astuti, “Analisis Sentimen Review Produk Skincare Dengan Naïve Bayes Classifier Berbasis Particle Swarm Optimization (PSO),” J. Media Inform. Budidarma, vol. 6, no. 4, p. 1806, 2022, doi: 10.30865/mib.v6i4.4119.
[13] 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. 1132–1138, 2023.
[14] A. S. Chan, F. Fachrizal, and A. R. Lubis, “Outcome Prediction Using Naïve Bayes Algorithm in the Selection of Role Hero Mobile Legend,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Jul. 2020, pp. 1–6. doi:
10.1088/1742-6596/1566/1/012041.
[15] 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.
[16] 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, Naïve 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.
[17] 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. 1–26, 2023, doi: 10.3390/su15043170.
[18] S. M. Listijo, T. Purwani, S. T. Galih, and T. Hafidzin, “Prediksi Kemenangan Dan Susunan Tim Pada Game Mobile Legends Bang Bang Menggunakan Algoritma Naïve Bayes,” Komputaki , vol. 6, no. 1, pp. 15–17, 2020.
[19] 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
[20] N. Salmi and Z. Rustam, “Naïve 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.
[21] K. Khadijah, N. Sabilly, and F. A. Nugroho, “Sentiment Analysis of League of Legends: Wild Rift Reviews on Google Play Using Naã Ve Bayes Classifier,” J. Ilm. Kursor, vol. 12, no. 1, pp. 23–30, 2023, [Online]. Available:
http://kursorjournal.org/index.php/kursor/article/view/328%0Ahttp://kursorjournal.org/index.php/kursor/article/downlo ad/328/147
[22] 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. 109–122, 2020, doi: 10.21609/jiki.v13i2.885.
[23] 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. 277–288, 2023, doi: 10.22219/kinetik.v8i3.1740.
[24] 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. 141–149, 2020, doi:
10.35746/jtim.v2i3.102.
[25] A. P. Wibawa et al., “Naïve 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.
[26] M. F. Sarifah, “Naive bayes algorithm performance for smartphone sentiment analysis in social media,” Int. J. Artif.
Intell. Informatics, vol. 2, no. 2, pp. 98–107, 2022, doi: 10.33292/ijarlit.v2i2.41.
[27] 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. 5198–5219, 2021, doi: 10.1007/s11227-020-03481-x.
[28] M. Alehegn, R. R. Joshi, and P. Mulay, “Diabetes analysis and prediction using random forest, KNN, Naïve Bayes, and J48: An ensemble approach,” Int. J. Sci. Technol. Res., vol. 8, no. 9, pp. 1346–1354, 2019.
[29] K. Lemons, “A Comparison Between Naïve Bayes and Random Forest to Predict Breast Cancer IJURCA : International Journal of Undergraduate Research & Creative Activities A Comparison Between Naïve Bayes and Random Forest to Predict,” vol. 12, no. 1, 2023.
[30] 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. 525–531, 2019, doi: 10.5220/0008364105250531.
[31] N. T. M. Hazmiza, “Classifying Violent Elements in Role-Playing Games Based on User Review using Naïve Bayes Technique,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 1.3, pp. 402–407, 2020, doi:
10.30534/ijatcse/2020/6391.32020.
