Tugas Akhir Mahasiswa NIM 12220065

research
  • 12 Oct
  • 2026

Tugas Akhir Mahasiswa NIM 12220065

Abstrak. Mobile Legends: Bang Bang (MLBB) memiliki kompetisi profesional melalui Mobile Legends Professional League (MPL). Prediksi kemenangan berdasarkan fase hero draft menjadi salah satu masalah yang dapat dianalisis menggunakan machine learning. Penelitian ini menerapkan algoritma Random Forest untuk memprediksi kemenangan pertandingan MPL Indonesia Season 16 menggunakan 207 data pertandingan yang diperoleh dari Liquipedia. Tahapan penelitian meliputi pengumpulan data, preprocessing, pembangunan model pada Altair AI Studio (RapidMiner), tuning parameter, dan evaluasi menggunakan 10-fold Cross Validation. Konfigurasi akhir menggunakan 1200 trees, criterion Gini Index, maximal depth 20, subset ratio 0,6, serta prepruning. Hasil pengujian menunjukkan akurasi 70,07% ± 8,83% dengan micro average 70,05%, meningkat dari konfigurasi awal 57,02%. Hasil tersebut menunjukkan bahwa Random Forest dapat digunakan untuk memprediksi kemenangan berdasarkan atribut yang digunakan, meskipun faktor di luar dataset tetap dapat memengaruhi hasil pertandingan.

Abstract. Mobile Legends: Bang Bang (MLBB) features a professional competition through the Mobile Legends Professional League (MPL). Predicting match outcomes from the hero draft phase is a problem that can be analyzed using machine learning. This study applies the Random Forest algorithm to predict MPL Indonesia Season 16 match outcomes using 207 matches collected from Liquipedia. The research stages include data collection, preprocessing, model development in Altair AI Studio (RapidMiner), parameter tuning, and evaluation using 10-fold Cross Validation. The final configuration uses 1,200 trees, the Gini Index criterion, a maximum depth of 20, a subset ratio of 0.6, and pre-pruning. The model achieved 70.07% ± 8.83% accuracy with a 70.05% micro average, improving from the initial 57.02% configuration. These findings indicate that Random Forest can be used to predict match outcomes based on the attributes analyzed, although factors outside the dataset may still influence match results.

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