Alzuhdi, Akmal Vrisna (2024) Optimizing random forest algorithm to classify player's memorisation via in-game data / Akmal Vrisna Alzuhdi. Diploma thesis, Universitas Negeri Malang.
Full text not available from this repository.Abstract
p 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. /p
| Item Type: | Thesis (Diploma) |
|---|---|
| Divisions: | Fakultas Teknik (FT) > Departemen Teknik Elektro (TE) > S1 Teknik Informatika |
| Depositing User: | library UM |
| Date Deposited: | 05 Nov 2024 04:29 |
| Last Modified: | 24 Feb 2026 06:35 |
| URI: | http://repository.um.ac.id/id/eprint/400270 |
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