Skripsi

Optimizing random forest algorithm to classify player\'s memorisation via in-game data / Akmal Vrisna Alzuhdi

Abstrak
Assesment of a player s knowledge in game education has been around for some time. We argue that traditional evaluation in and around a gaming session may disrupt the players immersion. This research aims to construct a non-invasive prediction of a game education players Memorization via in-game data using an optimized Random Forest. Firstly we obtained dataset from a 3-months survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next we generated three variants of dataset via the preprocessing stages resampling method (SMOTE) normalization (min-max) and a combination of resampling and normalization. Then we trained and optimized three Random Forest (RF) classifiers to predict the player s Memorization. We chose RF because it can generalize well given the high dimensional dataset. We used RF as the classifier subject to optimization using its hyperparameter n_estimators. We implemented a Grid Search Cross Validation (GSCV) method to identify the best value of n_estimators. We utilized the statistics of GSCV results to reduce the weight of n_estimators by observing the region of interest shown by the graphs of perfomance of the classifiers.Overall the classifiers fitted using the BEST n_estimators from GSCV perfomed well with around 80% accuracy. Moreover we successfully identified the smaller number of n_estimators (OPTIMAL) at least halved the BEST n_estimators. All classifiers were retrained using the OPTIMAL n_estimators. We found out that the perfomance of the classifiers were relatively steady at 80%. This means that we successfully optimized the Random Forest in predicting a player s Memorization when playing Chem Fight game. An automated technique presented in this paper can monitor student interactions and evaluate their abilities based on in-game data. As such it can offer objective data about the skills used.

Informasi Detail

DDC

SKRIPSI DIGITAL

Prodi

Universitas Negeri Malang. Program Studi Teknik Informatika, 2024.

Deskripsi Fisik

10 hlm. : ilus.

Bahasa

Indonesia

No Reg

6167/RS/24

Edisi

Skripsi (Sarjana)--Universitas Negeri Malang. 2024

Subjek

1. GAME EDUKASI - RANDOM FOREST
2. EDUCATIONAL GAME - RANDOM FOREST

Pembimbing

1. Harits Ar Rosyid, S.t., M.t., Ph.d

Lampiran Berkas