Short Message Service (SMS) Spam Filtering using Machine Learning in Bahasa Indonesia

Short Message Service (SMS) is an essential communication tool in Indonesian society. Companies use SMS as a promotion tool but unfortunately some individuals use SMS to send spam messages. A smartphone user in Indonesia has had an experience with these spam and promotional messages. This study presents a model to classify spam, promotion and ham messages based on Indonesian text messages. The model was trained with 4,125 text messages, tested with 1,260 text messages. A 10-fold cross validation method was used to evaluate the classifiers and the results show that Random Forest (94.62%), Multinomial Logistic Regression (94.57%), Support Vector Machine (94.38%), and XGBoost (94.52%) are among the best models to be used for a multiclass SMS classification.