Penggunaan Algoritma Gaussian Naïve Bayes & Decision Tree Untuk Klasifikasi Tingkat Kemenangan Pada Game Mobile Legends
DOI:
https://doi.org/10.53842/juki.v6i1.472Keywords:
Mobile Legends, Classification, Gaussian Naïve Bayes, Decision Tree, Draft PickAbstract
The development of technology and the internet has increased the popularity of online games, such as Mobile Legends. However, in competitions, players often experience defeat due to various factors, including player skills, team strategies, and the right hero selection. The right hero selection is very important to increase the chances of winning. Therefore, the Mobile Legends Professional League (MPL) has become a focus for competitive teams around the world. This study aimed to determine the classification of victory in MPL matches based on draft pick. Gaussian Naïve Bayes and Decision Tree were used as classification algorithm models in this study. The process in this study included cleaning data, data transformation (labeling), handling imbalanced data, scaling, splitting, and hyperparameter. The evaluation stage used confusion matrix, correlation data, and AU-ROC curve. The results of this study showed that the Decision Tree method had better performance than Gaussian Naïve Bayes in classifying data using the confusion matrix. The AUC (area under the receiver operating characteristic curve) analysis showed that the decision tree had better performance than Gaussian naive Bayes in predicting positive and negative data. This is indicated by the higher AUC value for the Decision Tree, which is 0.67 compared to Gaussian Naïve Bayes which is 0.48. Classification models with higher AUC values can more accurately distinguish between positive and negative data. In this study, the Decision Tree had a higher AUC value than Gaussian Naïve Bayes so the Decision Tree could more accurately classify victory and defeat data.
Downloads
References
[ 1 ] M. Huda, “Analisis User Experience Pada Game Mobile Legend Versi 1.4.14.4454 Dengan Menggunakan Game-Design Factor Questionnaire,” Jurnal Ekonomi Dan Teknik Informatika, vol. 8, p. 25, 2020.
[ 2 ] C. V. Wijaya and S. Paramita, “Komunikasi Virtual dalam Game Online (Studi Kasus dalam Game Mobile Legends),” Koneksi, vol. 3, p. 262, 2019.
[ 3 ] R. Fieter, D. Ritzky, and K. M. R. Brahmana, “Pengaruh Online Experience Terhadap Loyalty Melalui Satisfaction Pemain Mobile Legends,” Agora, vol. 6, no. 2, 2018.
[ 4 ] A. Bahtiar, R. R. Muhima, D. A. Rachman, I. T. Adhi, and T. Surabaya, “Penerapan Model Spiral Pada Rancang Bangun Game Platformer,” in Seminar Nasional Sains dan Teknologi Terapan VII, 2019, p. 601.
[ 5 ] Tonggi Simanjuntak, “Jumlah Pemain Game Mobile Legends di 2023 Melonjak Drastis!,” revivaltv.id. Accessed: Jan. 14, 2024. [Online]. Available: https://revivaltv.id/news/mlbb/jumlah-pemain-game-mobile-legends-2023
[ 6 ] A. Haris, D. C. Anggraini, and D. Mardiana, “Pengaruh Game Online Terhadap Ketaatan Beribadah Mahasiswa Di Jurusan Pendidikan Agama Islam Universitas Muhammadiyah Malang,” Jurnal Visi Ilmu Pendidikan, vol. 13, no. 2, p. 99, Sep. 2021, doi: 10.26418/jvip.v13i2.43475.
[ 7 ] 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.
[ 8 ] A. T. Susilo, H. Setiawan, R. A. Saputro, T. Purwadi, and A. Saifudin, “Penggunaan Metode Naïve Bayes untuk Memprediksi Tingkat Kemenangan pada Game Mobile Legends,” Jurnal Teknologi Sistem Informasi dan Aplikasi, vol. 4, no. 1, p. 46, Jan. 2021, doi: 10.32493/jtsi.v4i1.7807.
[ 9 ] F. Saputra, F. Dwikotjo, and S. Sumantyo, “Pengaruh Sistem Informasi Manajemen: Kepuasan Konsumen dan Keputusan Pembelian Tiket MPL Mobile Legend di Aplikasi Blibli.com,” Jurnal Kewirausahaan dan Manajemen Bisnis, vol. 1, no. 2, 2023.
[ 10 ] Alfa Rizki, “Berapa prize pool MPL ID S12?,” oneesports. Accessed: Oct. 10, 2023. [Online]. Available: https://www.oneesports.id/mobile-legends/prize-pool-mpl-id-s12/
[ 11 ] A. C. Putro, “Sistem Prediksi Kemenangan Tim Pada Game Mobile Legends Dengan Metode Naive Bayes Mobile Legends Win Prediction Using Naive Bayes,” Untag, Pp. 1–2, 2018.
[ 12 ] A. I. Marpaung, “Pengaruh Promosi Dan Persepsi Harga Terhadap Minat Beli Kembali Diamond Game Mobile Legends Di Kota Medan,” Universitas HKBP Nonmesen Ekonomi Manajemen , Nov. 2022.
[ 13 ] G. A. Sandag, “Model Prediksi Kemenangan Tim dalam Game League of Legend Menggunakan Algoritma Decision Tree,” Jurnal Komputer Terapan, vol. 7, no. 1, 2021, [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/
[ 14 ] A. Dharmawan, J. Gondohanindijo, Y. Prihati, and T. Hafidzin, “Optimization Of Players And Game Win Prediction Using Naïve Bayes Algorithm,” Jurnal Elektro Luceat, vol. 8, no. 1, 2022.
[ 15 ] G. Sani, G. Prawira, and H. Setiaji, “Penerapan Data Transformation Pada Database Sistem Informasi Manajemen Rumah Sakit,” Sintak, 2019.
[ 16 ] S. Mutmainah, “Penanganan Imbalance Data Pada Klasifikasi Kemungkinan Penyakit Stroke,” Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi, vol. 1, no. 1, 2021, doi: 10.20885/snati.v1i1.2.
[ 17 ] P. Singh, Learn PySpark. Apress, 2019. doi: 10.1007/978-1-4842-4961-1.
[ 18 ] N. A’ayunnisa, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” Indonesian Journal of Data and Science (IJODAS), vol. 3, no. 3, pp. 115–121, 2022.
[ 19 ] D. Gunawan, D. Riana, D. Ardiansyah, F. Akbar, and S. Alfarizi, “Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023”, doi: 10.31294/jtk.v4i2.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Yoga Naufal Ray Putro, Aidil Afriansyah, Radhinka Bagaskara
This work is licensed under a Creative Commons Attribution 4.0 International License.