Predicting the Gender of Indonesian Names

We present a new way to predict genders from names using character-level LongShort Term Memory (char-LSTM). We compared our method with some conventional machine learning methods, namely Näıve 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. The results show that we can achieve 92.25% accuracy by using this approach.