Abstract—Classification using machine learning is an
alternative that is widely used to classify data. There are various
classification methods or also known as machine learning
classification algorithms that can be used. However, to get the
best classification results, we need a classifier that fits the dataset
type to provide the best classification performance. In addition,
the quality and quantity of data contained in a dataset also has
an influence on the classification performance. In this study,
several attempts were made to improve the classification
performance of the dataset of Indonesian language exam
questions at the elementary school level based on the category of
difficulty level. The efforts made consist of improving the quality
of the dataset and using the StringToWordVector filter algorithm
to manage textual data, as well as the use of several classification
algorithms such as the nave Bayes algorithm, Random Forest,
and REPTree. Classification is done by using WEKA Tools. The
results of the experiments carried out showed the highest
performance increase of 15% after improving the quality of the
dataset and using the right classification method.