  We investigated a way to predict the gender of a name using character-level
Long-Short Term Memory (char-LSTM). We compared our method with some
conventional machine learning methods, namely Naive Bayes, logistic regression,
and XGBoost with n-grams as the features. We evaluated the models on a dataset
consisting of the names of Indonesian people. It is not common to use a family
name as the surname in Indonesian culture, except in some ethnicities.
Therefore, we inferred the gender from both full names and first names. The
results show that we can achieve 92.25% accuracy from full names, while using
first names only yields 90.65% accuracy. These results are better than the ones
from applying the classical machine learning algorithms to n-grams.
